r "Sex" ) -result <- basic_table(show_colcounts = TRUE) %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% +result <- basic_table(show_colcounts = TRUE) %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) @@ -240,14 +210,14 @@ Including Missing Values in r adsl$SEX[adsl$SEX == "M"] <- NA adsl <- df_explicit_na(adsl, na_level = "Missing Values") -result <- basic_table(show_colcounts = TRUE) %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% +result <- basic_table(show_colcounts = TRUE) %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) @@ -283,14 +253,14 @@ Missing Values in Numeric Variables "Sex" ) -result <- basic_table(show_colcounts = TRUE) %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% +result <- basic_table(show_colcounts = TRUE) %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) diff --git a/main/articles/tables.html b/main/articles/tables.html index 5632e2dd5a..faf9101a1c 100644 --- a/main/articles/tables.html +++ b/main/articles/tables.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -122,7 +92,7 @@ analyses from clinical trials in R. The core functionality for tabulation is built on the more general purpose rtables package. New users should first begin by reading the “Introduction -to tern” and “Introduction +to tern” and “Introduction to rtables” vignettes. The packages used in this vignette are: @@ -143,10 +113,10 @@ rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analyze functions are wrappers around -rtables::analyze function, they offer various methods +rtables::analyze function, they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analyze functions are @@ -173,26 +143,26 @@ Internals of tern Anal rcell type formatting themselves. formatted analysis functions a_*. These have the same arguments as the corresponding statistics functions, and can be further -customized by calling rtables::make_afun() on them. They -are used as afun in rtables::analyze(). +customized by calling rtables::make_afun() on them. They +are used as afun in rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions are used in combination with the rtables layout functions, in the pipeline which creates the table. They are the last element of the chain. -We will use the native rtables::analyze function with +We will use the native rtables::analyze function with the tern formatted analysis functions as a afun parameter. -l <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = a_summary) +l <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = a_summary) -build_table(l, df = adrs) -The rtables::make_afun function is helpful when somebody +build_table(l, df = adrs) +The rtables::make_afun function is helpful when somebody wants to attach some format to the formatted analysis function. -afun <- make_afun( +afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = "xx."), @@ -200,12 +170,12 @@ Internals of tern Anal .indent_mods = c(median = 1L) ) -l2 <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = afun) +l2 <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = afun) -build_table(l2, df = adrs) +build_table(l2, df = adrs) @@ -260,15 +230,15 @@ Demographic Tablevars <- c("AGE", "SEX") var_labels <- c("Age (yr)", "Sex") -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— @@ -295,15 +265,15 @@ Demographic Table# Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c("M", "F", "U", "UNDIFFERENTIATED")) -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— @@ -323,8 +293,8 @@ Demographic Tableanalyze(), analyze_colvars() and -summarize_row_groups() and provide options for easy +analyze(), analyze_colvars() and +summarize_row_groups() and provide options for easy formatting and analysis modifications. To customize the display for the demographics table, we can do so via the arguments in analyze_vars(). Most layout creating @@ -343,17 +313,17 @@ Demographic Table # Select statistics and modify default formats. -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels, .stats = c("n", "mean_sd", "count"), .formats = c(mean_sd = "xx.xx (xx.xx)") ) %>% - build_table(adsl) + build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ———————————————————————————————————————————————————————————————————————————————— @@ -371,16 +341,16 @@ Demographic Table -lyt <- basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients(): -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred. a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -236,17 +206,17 @@ ArgumentsValue count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout. s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag. -a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE. a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,16 +191,16 @@ ArgumentsValue count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout. s_count_values() returns output of s_summary() for specified values of a non-numeric variable. -a_count_values() returns the corresponding list with formatted rtables::CellValue(). +a_count_values() returns the corresponding list with formatted rtables::CellValue(). Functions count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_values(): S3 generic function to count values. s_count_values(character): Method for character class. s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -212,7 +182,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -238,10 +208,10 @@ ArgumentsValue summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout. s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s). -a_coxreg() returns formatted rtables::CellValue(). +a_coxreg() returns formatted rtables::CellValue(). Details @@ -257,11 +227,11 @@ Functionsrtables::analyze_colvars() and rtables::summarize_row_groups(). +is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups(). s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list. -a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg(). +a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg(). See also diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html index 6124a8f4c1..b15b4c925c 100644 --- a/main/reference/cox_regression_inter.html +++ b/main/reference/cox_regression_inter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -210,7 +180,7 @@ Note Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table(). +variables before passing the dataset to rtables::build_table(). diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html index 976e7c3dcc..c3759b2a0d 100644 --- a/main/reference/cut_quantile_bins.html +++ b/main/reference/cut_quantile_bins.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html index 5b9e065ed8..9cba629d12 100644 --- a/main/reference/d_count_abnormal_by_baseline.html +++ b/main/reference/d_count_abnormal_by_baseline.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html index 8f5e09613a..ed30a89198 100644 --- a/main/reference/d_count_cumulative.html +++ b/main/reference/d_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_missed_doses.html b/main/reference/d_count_missed_doses.html index 3983179580..88d7cb115f 100644 --- a/main/reference/d_count_missed_doses.html +++ b/main/reference/d_count_missed_doses.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_onco_rsp_label.html b/main/reference/d_onco_rsp_label.html index 6ad11d55b2..36eb740b33 100644 --- a/main/reference/d_onco_rsp_label.html +++ b/main/reference/d_onco_rsp_label.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html index 50dd3d760f..911054124b 100644 --- a/main/reference/d_pkparam.html +++ b/main/reference/d_pkparam.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html index 45eecb0d3c..8a8f84d672 100644 --- a/main/reference/d_proportion.html +++ b/main/reference/d_proportion.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html index 513d2d47ae..fc4b954e11 100644 --- a/main/reference/d_proportion_diff.html +++ b/main/reference/d_proportion_diff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html index 953122b65f..cdcbc5e18c 100644 --- a/main/reference/d_rsp_subgroups_colvars.html +++ b/main/reference/d_rsp_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html index 8974168b2e..ad93487971 100644 --- a/main/reference/d_survival_subgroups_colvars.html +++ b/main/reference/d_survival_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html index f32963f964..3c31b90c80 100644 --- a/main/reference/d_test_proportion_diff.html +++ b/main/reference/d_test_proportion_diff.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/day2month.html b/main/reference/day2month.html index 4cdf9eb8d3..00a2059911 100644 --- a/main/reference/day2month.html +++ b/main/reference/day2month.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob.html b/main/reference/decorate_grob.html index 05bab36561..e0ee545cc9 100644 --- a/main/reference/decorate_grob.html +++ b/main/reference/decorate_grob.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_factory.html b/main/reference/decorate_grob_factory.html index bf15634600..c128f2d675 100644 --- a/main/reference/decorate_grob_factory.html +++ b/main/reference/decorate_grob_factory.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_set.html b/main/reference/decorate_grob_set.html index 5193dd3c1b..0de85e9334 100644 --- a/main/reference/decorate_grob_set.html +++ b/main/reference/decorate_grob_set.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_na_str.html b/main/reference/default_na_str.html index 53dc58920e..406deecf68 100644 --- a/main/reference/default_na_str.html +++ b/main/reference/default_na_str.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_stats_formats_labels.html b/main/reference/default_stats_formats_labels.html index 9969f93d9f..f98b60bff5 100644 --- a/main/reference/default_stats_formats_labels.html +++ b/main/reference/default_stats_formats_labels.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,7 +193,7 @@ Note These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings. Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables"). +return a string. This is well documented in vignette("custom_appearance", package = "rtables"). See also diff --git a/main/reference/desctools_binom.html b/main/reference/desctools_binom.html index 4b01634fe0..6be9e52e5c 100644 --- a/main/reference/desctools_binom.html +++ b/main/reference/desctools_binom.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df2gg.html b/main/reference/df2gg.html index d2a0c06ec1..3459218bc2 100644 --- a/main/reference/df2gg.html +++ b/main/reference/df2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df_explicit_na.html b/main/reference/df_explicit_na.html index 7239b2d0e5..3e4a639463 100644 --- a/main/reference/df_explicit_na.html +++ b/main/reference/df_explicit_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/dot-is_equal_float.html b/main/reference/dot-is_equal_float.html index 0e13761934..6c1e53f091 100644 --- a/main/reference/dot-is_equal_float.html +++ b/main/reference/dot-is_equal_float.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/draw_grob.html b/main/reference/draw_grob.html index 451b8e7fd6..f6cdcb0a01 100644 --- a/main/reference/draw_grob.html +++ b/main/reference/draw_grob.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/empty_vector_if_na.html b/main/reference/empty_vector_if_na.html index 7607d61187..452a306105 100644 --- a/main/reference/empty_vector_if_na.html +++ b/main/reference/empty_vector_if_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_coef.html b/main/reference/estimate_coef.html index 2bc5525f87..ce5485013a 100644 --- a/main/reference/estimate_coef.html +++ b/main/reference/estimate_coef.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_multinomial_rsp.html b/main/reference/estimate_multinomial_rsp.html index c917481057..4960eb095e 100644 --- a/main/reference/estimate_multinomial_rsp.html +++ b/main/reference/estimate_multinomial_rsp.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -177,17 +147,17 @@ ArgumentsValue estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout. s_length_proportion() returns statistics from s_proportion(). -a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::analyze() and +rtables::summarize_row_groups(). s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion(). a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -244,17 +214,17 @@ ArgumentsValue estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout. s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable. -a_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion(): Statistics function estimating a proportion along with its confidence interval. a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html index a19da4827a..75158ed039 100644 --- a/main/reference/explicit_na.html +++ b/main/reference/explicit_na.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html index 890e00c7b2..7f4ea02325 100644 --- a/main/reference/extract_by_name.html +++ b/main/reference/extract_by_name.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_biomarkers.html b/main/reference/extract_rsp_biomarkers.html index 47f4183173..22d0b045c8 100644 --- a/main/reference/extract_rsp_biomarkers.html +++ b/main/reference/extract_rsp_biomarkers.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_subgroups.html b/main/reference/extract_rsp_subgroups.html index 10e7fd68dd..bca8059217 100644 --- a/main/reference/extract_rsp_subgroups.html +++ b/main/reference/extract_rsp_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_biomarkers.html b/main/reference/extract_survival_biomarkers.html index 4756961aa7..353e5cea60 100644 --- a/main/reference/extract_survival_biomarkers.html +++ b/main/reference/extract_survival_biomarkers.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_subgroups.html b/main/reference/extract_survival_subgroups.html index 825bc1eedd..fc3a8da8f2 100644 --- a/main/reference/extract_survival_subgroups.html +++ b/main/reference/extract_survival_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extreme_format.html b/main/reference/extreme_format.html index cfb5075e63..c363e0dcb0 100644 --- a/main/reference/extreme_format.html +++ b/main/reference/extreme_format.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html index a2fa7874b0..e59e2a03ae 100644 --- a/main/reference/f_conf_level.html +++ b/main/reference/f_conf_level.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html index 1c4fea37df..5d7ab493ce 100644 --- a/main/reference/f_pval.html +++ b/main/reference/f_pval.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html index 83ab24eb82..f332240368 100644 --- a/main/reference/fct_collapse_only.html +++ b/main/reference/fct_collapse_only.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html index d7fb3b6c64..4cac1ccba5 100644 --- a/main/reference/fct_discard.html +++ b/main/reference/fct_discard.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_explicit_na_if.html b/main/reference/fct_explicit_na_if.html index 3bcaf94176..6848982dde 100644 --- a/main/reference/fct_explicit_na_if.html +++ b/main/reference/fct_explicit_na_if.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_coxreg.html b/main/reference/fit_coxreg.html index 14c5d95f6b..5c2bfc26a0 100644 --- a/main/reference/fit_coxreg.html +++ b/main/reference/fit_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html index 0f364c07c5..4e6119b249 100644 --- a/main/reference/fit_logistic.html +++ b/main/reference/fit_logistic.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html index f5d42bfe7c..5f64d2f786 100644 --- a/main/reference/fit_rsp_step.html +++ b/main/reference/fit_rsp_step.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html index b7da54c090..ca68ad9a04 100644 --- a/main/reference/fit_survival_step.html +++ b/main/reference/fit_survival_step.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html index 2e578ce576..964ab27f65 100644 --- a/main/reference/forest_viewport.html +++ b/main/reference/forest_viewport.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html index 66cff1543b..f5b5ca5633 100644 --- a/main/reference/format_auto.html +++ b/main/reference/format_auto.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -106,7 +76,7 @@ Argumentsdt_var (numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered. diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html index 7c89de6365..4559bb997c 100644 --- a/main/reference/format_count_fraction.html +++ b/main/reference/format_count_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html index fb67db8371..ae6e985489 100644 --- a/main/reference/format_count_fraction_fixed_dp.html +++ b/main/reference/format_count_fraction_fixed_dp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html index 9c0e167253..ce2f2e82e2 100644 --- a/main/reference/format_count_fraction_lt10.html +++ b/main/reference/format_count_fraction_lt10.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html index c12a059e98..6fdb3dd26f 100644 --- a/main/reference/format_extreme_values.html +++ b/main/reference/format_extreme_values.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html index dea1549bfe..c60b369a7d 100644 --- a/main/reference/format_extreme_values_ci.html +++ b/main/reference/format_extreme_values_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html index 8d4274cffb..2c2145230d 100644 --- a/main/reference/format_fraction.html +++ b/main/reference/format_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html index 297df429d5..3c5ba714ad 100644 --- a/main/reference/format_fraction_fixed_dp.html +++ b/main/reference/format_fraction_fixed_dp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_threshold.html b/main/reference/format_fraction_threshold.html index fc7c8ddea7..def9b6a40e 100644 --- a/main/reference/format_fraction_threshold.html +++ b/main/reference/format_fraction_threshold.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_sigfig.html b/main/reference/format_sigfig.html index 02a2b8591e..692c3a6abc 100644 --- a/main/reference/format_sigfig.html +++ b/main/reference/format_sigfig.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_xx.html b/main/reference/format_xx.html index 0ed5c9a3d8..18bbe09129 100644 --- a/main/reference/format_xx.html +++ b/main/reference/format_xx.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html index d325263a97..13af4631b1 100644 --- a/main/reference/formatting_functions.html +++ b/main/reference/formatting_functions.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_forest.html b/main/reference/g_forest.html index 3c11db2339..90308fb9b9 100644 --- a/main/reference/g_forest.html +++ b/main/reference/g_forest.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,7 +191,7 @@ Value Details - Given a rtables::rtable() object with at least one column with a single value and one column with 2 + Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side. diff --git a/main/reference/g_ipp.html b/main/reference/g_ipp.html index a0fe570f3e..187580ab44 100644 --- a/main/reference/g_ipp.html +++ b/main/reference/g_ipp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_km.html b/main/reference/g_km.html index cdfc1967d3..f352196584 100644 --- a/main/reference/g_km.html +++ b/main/reference/g_km.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html index 19a73c87f9..f690b652d5 100644 --- a/main/reference/g_lineplot.html +++ b/main/reference/g_lineplot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_step.html b/main/reference/g_step.html index c610853a23..eb4f82b05a 100644 --- a/main/reference/g_step.html +++ b/main/reference/g_step.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html index cfd51f294f..de2d4f49d4 100644 --- a/main/reference/g_waterfall.html +++ b/main/reference/g_waterfall.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_covariates.html b/main/reference/get_covariates.html index f17bf37bfe..ed76800bad 100644 --- a/main/reference/get_covariates.html +++ b/main/reference/get_covariates.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html index 6214ffa6c9..6215eb9b9d 100644 --- a/main/reference/get_smooths.html +++ b/main/reference/get_smooths.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html index 6e3a623ff0..b5b046e9bb 100644 --- a/main/reference/groups_list_to_df.html +++ b/main/reference/groups_list_to_df.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -76,7 +46,7 @@ - This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). + This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). diff --git a/main/reference/h_adlb_abnormal_by_worst_grade.html b/main/reference/h_adlb_abnormal_by_worst_grade.html index 924b3126fd..124a0f60f6 100644 --- a/main/reference/h_adlb_abnormal_by_worst_grade.html +++ b/main/reference/h_adlb_abnormal_by_worst_grade.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html index c86690c603..a1da8de34a 100644 --- a/main/reference/h_adlb_worsen.html +++ b/main/reference/h_adlb_worsen.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html index 5fc0bf7381..b1b901e4e0 100644 --- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html +++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ancova.html b/main/reference/h_ancova.html index 27bf03975f..59f11617c8 100644 --- a/main/reference/h_ancova.html +++ b/main/reference/h_ancova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_append_grade_groups.html b/main/reference/h_append_grade_groups.html index 2776a060c9..40ba81d7bd 100644 --- a/main/reference/h_append_grade_groups.html +++ b/main/reference/h_append_grade_groups.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html index 06b97cf510..cbaecea676 100644 --- a/main/reference/h_col_indices.html +++ b/main/reference/h_col_indices.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html index 36c0c9a1e1..5d58afe576 100644 --- a/main/reference/h_count_cumulative.html +++ b/main/reference/h_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html index 4be3ea8680..2300696d50 100644 --- a/main/reference/h_cox_regression.html +++ b/main/reference/h_cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html index 7ac8b43b49..54b0d7080d 100644 --- a/main/reference/h_data_plot.html +++ b/main/reference/h_data_plot.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html index 4cf323ad4e..96502e49d1 100644 --- a/main/reference/h_decompose_gg.html +++ b/main/reference/h_decompose_gg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html index 1033ff5a0c..11071684bb 100644 --- a/main/reference/h_format_row.html +++ b/main/reference/h_format_row.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -95,7 +65,7 @@ Argumentsformat (named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter. diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html index ba609f3d69..d4c8203cba 100644 --- a/main/reference/h_g_ipp.html +++ b/main/reference/h_g_ipp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ggkm.html b/main/reference/h_ggkm.html index 512f6a9180..0aac0f9127 100644 --- a/main/reference/h_ggkm.html +++ b/main/reference/h_ggkm.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_glm_count.html b/main/reference/h_glm_count.html index 036db54acf..74a4e7900b 100644 --- a/main/reference/h_glm_count.html +++ b/main/reference/h_glm_count.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html index 7dcead7d01..132d15c26a 100644 --- a/main/reference/h_grob_coxph.html +++ b/main/reference/h_grob_coxph.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html index 82ea2d549f..60bd8ff8d5 100644 --- a/main/reference/h_grob_median_surv.html +++ b/main/reference/h_grob_median_surv.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html index 3af28455fb..7879a81306 100644 --- a/main/reference/h_grob_tbl_at_risk.html +++ b/main/reference/h_grob_tbl_at_risk.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html index 48a9227ad3..dd46627d90 100644 --- a/main/reference/h_grob_y_annot.html +++ b/main/reference/h_grob_y_annot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html index 77eb4bd003..e85621d716 100644 --- a/main/reference/h_incidence_rate.html +++ b/main/reference/h_incidence_rate.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html index 9e3fdc86ed..67d651af4a 100644 --- a/main/reference/h_km_layout.html +++ b/main/reference/h_km_layout.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html index fe6cbfe730..8dd16febea 100644 --- a/main/reference/h_logistic_regression.html +++ b/main/reference/h_logistic_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html index 10495898fc..59f19b1f76 100644 --- a/main/reference/h_map_for_count_abnormal.html +++ b/main/reference/h_map_for_count_abnormal.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html index e1e17eedc3..b93837e390 100644 --- a/main/reference/h_odds_ratio.html +++ b/main/reference/h_odds_ratio.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html index 982b2989b6..318bbf2ecc 100644 --- a/main/reference/h_pkparam_sort.html +++ b/main/reference/h_pkparam_sort.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ppmeans.html b/main/reference/h_ppmeans.html index 0ce7208de8..57fe7b7773 100644 --- a/main/reference/h_ppmeans.html +++ b/main/reference/h_ppmeans.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html index 82c09a19d9..df5cd7a011 100644 --- a/main/reference/h_prop_diff.html +++ b/main/reference/h_prop_diff.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html index 879561bbee..7ebaf8c228 100644 --- a/main/reference/h_prop_diff_test.html +++ b/main/reference/h_prop_diff_test.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_proportions.html b/main/reference/h_proportions.html index e2454b4cec..cae1aa23c0 100644 --- a/main/reference/h_proportions.html +++ b/main/reference/h_proportions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_biomarkers_subgroups.html b/main/reference/h_response_biomarkers_subgroups.html index 6253742dc3..d40c44e05a 100644 --- a/main/reference/h_response_biomarkers_subgroups.html +++ b/main/reference/h_response_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_subgroups.html b/main/reference/h_response_subgroups.html index f8553643c3..3277639d7f 100644 --- a/main/reference/h_response_subgroups.html +++ b/main/reference/h_response_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_by_subgroups.html b/main/reference/h_split_by_subgroups.html index 30b5234a3f..53bbd7df94 100644 --- a/main/reference/h_split_by_subgroups.html +++ b/main/reference/h_split_by_subgroups.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html index 1c85bd0acc..f531b50248 100644 --- a/main/reference/h_split_param.html +++ b/main/reference/h_split_param.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_stack_by_baskets.html b/main/reference/h_stack_by_baskets.html index 856a6bfda2..6f466ed562 100644 --- a/main/reference/h_stack_by_baskets.html +++ b/main/reference/h_stack_by_baskets.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_step.html b/main/reference/h_step.html index 22264810df..aed7400437 100644 --- a/main/reference/h_step.html +++ b/main/reference/h_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html index e2ef402488..24d8bdff48 100644 --- a/main/reference/h_survival_biomarkers_subgroups.html +++ b/main/reference/h_survival_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html index f45ef9d1ed..27a3f5c549 100644 --- a/main/reference/h_survival_duration_subgroups.html +++ b/main/reference/h_survival_duration_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html index ca44867180..75abc5d17b 100644 --- a/main/reference/h_tab_one_biomarker.html +++ b/main/reference/h_tab_one_biomarker.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups(). +This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html index fb85cb68ff..d06f1ec802 100644 --- a/main/reference/h_tbl_coxph_pairwise.html +++ b/main/reference/h_tbl_coxph_pairwise.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html index 824c9b6ba7..6c36b258c8 100644 --- a/main/reference/h_tbl_median_surv.html +++ b/main/reference/h_tbl_median_surv.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html index bd3da774c9..75cee2d4f8 100644 --- a/main/reference/h_worsen_counter.html +++ b/main/reference/h_worsen_counter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html index 3b00b0578e..cdca04f44b 100644 --- a/main/reference/h_xticks.html +++ b/main/reference/h_xticks.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html index 4af234fef7..9793443aea 100644 --- a/main/reference/imputation_rule.html +++ b/main/reference/imputation_rule.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html index 54c857ff9c..c6a5c87c16 100644 --- a/main/reference/incidence_rate.html +++ b/main/reference/incidence_rate.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -239,7 +209,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -251,7 +221,7 @@ ArgumentsValue estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout. s_incidence_rate() returns the following statistics:person_years: Total person-years at risk. n_events: Total number of events observed. @@ -260,13 +230,13 @@ Valuen_unique: Total number of patients with at least one event observed. n_rate: Total number of events observed & estimated incidence rate. -a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). +a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval. a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate(). diff --git a/main/reference/index.html b/main/reference/index.html index 2a1665f99f..09b3124477 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -196,7 +166,7 @@ Analysis FunctionsAnalyze functions with their corresponding statistics functions and formatted analysis functions.Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table. Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves. -Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). +Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html index 3f1016c509..dd93cf81ce 100644 --- a/main/reference/labels_or_names.html +++ b/main/reference/labels_or_names.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/labels_use_control.html b/main/reference/labels_use_control.html index 5c301b4228..d4006f99f7 100644 --- a/main/reference/labels_use_control.html +++ b/main/reference/labels_use_control.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html index 7744090684..8accd2e89b 100644 --- a/main/reference/logistic_regression_cols.html +++ b/main/reference/logistic_regression_cols.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar(). +regression results. This function is a wrapper for rtables::split_cols_by_multivar(). diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html index 9807a24c22..45cb84b416 100644 --- a/main/reference/logistic_summary_by_flag.html +++ b/main/reference/logistic_summary_by_flag.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). +logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/make_names.html b/main/reference/make_names.html index d14c4af69d..ef3f426440 100644 --- a/main/reference/make_names.html +++ b/main/reference/make_names.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/month2day.html b/main/reference/month2day.html index 87004ac14c..bb59b07ac8 100644 --- a/main/reference/month2day.html +++ b/main/reference/month2day.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/muffled_car_anova.html b/main/reference/muffled_car_anova.html index 3d902766c3..e4c73d2ed5 100644 --- a/main/reference/muffled_car_anova.html +++ b/main/reference/muffled_car_anova.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/n_available.html b/main/reference/n_available.html index af70552f25..d8856b8358 100644 --- a/main/reference/n_available.html +++ b/main/reference/n_available.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html index 3bcb51179d..58a1801d4a 100644 --- a/main/reference/odds_ratio.html +++ b/main/reference/odds_ratio.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -228,17 +198,17 @@ ArgumentsValue estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout. s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot. -a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). +a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required. diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html index 51e9d86731..bd76dd200f 100644 --- a/main/reference/prop_diff.html +++ b/main/reference/prop_diff.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -237,16 +207,16 @@ ArgumentsValue estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout. s_proportion_diff() returns a named list of elements diff and diff_ci. -a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion. a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff(). diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html index 17bf534958..d4bfbab57f 100644 --- a/main/reference/prop_diff_test.html +++ b/main/reference/prop_diff_test.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,17 +187,17 @@ ArgumentsValue test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout. s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same. -a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_test_proportion_diff(): Statistics function which tests the difference between two proportions. a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff(). diff --git a/main/reference/prune_occurrences.html b/main/reference/prune_occurrences.html index 74e764167c..75f049f5b1 100644 --- a/main/reference/prune_occurrences.html +++ b/main/reference/prune_occurrences.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -137,7 +107,7 @@ Arguments Value -keep_rows() returns a pruning function that can be used with rtables::prune_table() +keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table. keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table. @@ -180,7 +150,7 @@ FunctionsNote Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface. +should be pruned, to conform with the rtables::prune_table() interface. diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html index ccf69253f9..8bcad27b16 100644 --- a/main/reference/range_noinf.html +++ b/main/reference/range_noinf.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html index 3369960273..c35e1c50c9 100644 --- a/main/reference/reapply_varlabels.html +++ b/main/reference/reapply_varlabels.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html index 53816fe9b9..4c72961f2a 100644 --- a/main/reference/response_biomarkers_subgroups.html +++ b/main/reference/response_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html index fb6d448883..a3f14ce1b6 100644 --- a/main/reference/response_subgroups.html +++ b/main/reference/response_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -171,7 +141,7 @@ Arguments Value An rtables table summarizing binary response by subgroup. -a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -182,8 +152,8 @@ DetailsFunctions tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups(). diff --git a/main/reference/rtable2gg.html b/main/reference/rtable2gg.html index 6068cb1bb8..cdf46a633f 100644 --- a/main/reference/rtable2gg.html +++ b/main/reference/rtable2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,7 +51,7 @@ -Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored. diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html index 0f6db1ef4c..67c8394ca2 100644 --- a/main/reference/rtables_access.html +++ b/main/reference/rtables_access.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html index 2198ad15a7..445c4fd861 100644 --- a/main/reference/s_cox_multivariate.html +++ b/main/reference/s_cox_multivariate.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html index fa683b677a..8c4b03b9ab 100644 --- a/main/reference/sas_na.html +++ b/main/reference/sas_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html index f5b86fe70a..3815c64608 100644 --- a/main/reference/score_occurrences.html +++ b/main/reference/score_occurrences.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Functionsrtables::cont_n_onecol() and rtables::cont_n_allcols(). +specified columns. Note that this is extending rtables::cont_n_onecol() and rtables::cont_n_allcols(). See also diff --git a/main/reference/split_cols_by_groups.html b/main/reference/split_cols_by_groups.html index 3c97f5cfee..f7bfcad622 100644 --- a/main/reference/split_cols_by_groups.html +++ b/main/reference/split_cols_by_groups.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -107,7 +77,7 @@ Arguments... -additional arguments to rtables::split_cols_by() in order. For instance, to +additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all). @@ -119,7 +89,7 @@ Value See also - rtables::split_cols_by() + rtables::split_cols_by() diff --git a/main/reference/split_text_grob.html b/main/reference/split_text_grob.html index 8671c0d8d3..062cbc39d2 100644 --- a/main/reference/split_text_grob.html +++ b/main/reference/split_text_grob.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html index fb97a58911..6f44c5774b 100644 --- a/main/reference/stack_grobs.html +++ b/main/reference/stack_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html index ffa8368b70..c31e318dd4 100644 --- a/main/reference/stat_mean_ci.html +++ b/main/reference/stat_mean_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html index ef90542a4e..6663e7ee57 100644 --- a/main/reference/stat_mean_pval.html +++ b/main/reference/stat_mean_pval.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_median_ci.html b/main/reference/stat_median_ci.html index ea5d28e072..f43024d65c 100644 --- a/main/reference/stat_median_ci.html +++ b/main/reference/stat_median_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_propdiff_ci.html b/main/reference/stat_propdiff_ci.html index b7119ac644..5db044c950 100644 --- a/main/reference/stat_propdiff_ci.html +++ b/main/reference/stat_propdiff_ci.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -141,7 +111,7 @@ Value See also - Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() + Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout. diff --git a/main/reference/strata_normal_quantile.html b/main/reference/strata_normal_quantile.html index afc45b01e1..82d6de6e3f 100644 --- a/main/reference/strata_normal_quantile.html +++ b/main/reference/strata_normal_quantile.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/study_arm.html b/main/reference/study_arm.html index 4a60dcedf5..67999fc7aa 100644 --- a/main/reference/study_arm.html +++ b/main/reference/study_arm.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/summarize_ancova.html b/main/reference/summarize_ancova.html index 1bf16f8b79..1eba34c24c 100644 --- a/main/reference/summarize_ancova.html +++ b/main/reference/summarize_ancova.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -254,7 +224,7 @@ ArgumentsValue summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout. s_ancova() returns a named list of 5 statistics:n: Count of complete sample size for the group. lsmean: Estimated marginal means in the group. @@ -264,13 +234,13 @@ Value pval: p-value (not adjusted for multiple comparisons). -a_ancova() returns the corresponding list with formatted rtables::CellValue(). +a_ancova() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_ancova(): Statistics function that produces a named list of results of the investigated linear model. a_ancova(): Formatted analysis function which is used as afun in summarize_ancova(). diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html index 70d34fcade..83fc422b89 100644 --- a/main/reference/summarize_change.html +++ b/main/reference/summarize_change.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -192,16 +162,16 @@ ArgumentsValue summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout. s_change_from_baseline() returns the same values returned by s_summary.numeric(). -a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits. a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change(). diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html index 5f7d10a3ef..2caae0ef34 100644 --- a/main/reference/summarize_colvars.html +++ b/main/reference/summarize_colvars.html @@ -2,13 +2,13 @@ Summarize variables in columns — summarize_colvars • tern @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -91,8 +61,8 @@ The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars(). -The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars(). +The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function. @@ -147,13 +117,13 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. See also - rtables::split_cols_by_multivar() and analyze_colvars_functions. + rtables::split_cols_by_multivar() and analyze_colvars_functions. diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html index 751617921c..34ebaf2237 100644 --- a/main/reference/summarize_functions.html +++ b/main/reference/summarize_functions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -78,7 +48,7 @@ - These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions + These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout: @@ -87,7 +57,7 @@ Details add_rowcounts() -estimate_multinomial_response() (with rtables::analyze()) +estimate_multinomial_response() (with rtables::analyze()) h_tab_one_biomarker() (probably to deprecate) logistic_summary_by_flag() summarize_num_patients() @@ -95,15 +65,15 @@ Detailssummarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() -Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table. See also -analyze_functions for functions which are wrappers for rtables::analyze(). -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +analyze_functions for functions which are wrappers for rtables::analyze(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). diff --git a/main/reference/summarize_glm_count.html b/main/reference/summarize_glm_count.html index 112cce09c8..6f424e2306 100644 --- a/main/reference/summarize_glm_count.html +++ b/main/reference/summarize_glm_count.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -252,7 +222,7 @@ ArgumentsValue summarize_glm_count() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_glm_count() to the table layout. s_glm_count() returns a named list of 5 statistics:n: Count of complete sample size for the group. rate: Estimated event rate per follow-up time. @@ -280,7 +250,7 @@ DetailsFunctions summarize_glm_count(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_glm_count(): Statistics function that produces a named list of results of the investigated Poisson model. diff --git a/main/reference/summarize_logistic.html b/main/reference/summarize_logistic.html index 328200b769..92bbe7b17b 100644 --- a/main/reference/summarize_logistic.html +++ b/main/reference/summarize_logistic.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -139,7 +109,7 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add a logistic regression variable summary to the table layout. diff --git a/main/reference/summarize_num_patients.html b/main/reference/summarize_num_patients.html index 275721ff71..1591a53f8f 100644 --- a/main/reference/summarize_num_patients.html +++ b/main/reference/summarize_num_patients.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -233,7 +203,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -255,10 +225,10 @@ ArgumentsValue analyze_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout. summarize_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout. s_num_patients() returns a named list of 3 statistics:unique: Vector of counts and percentages. nonunique: Vector of counts. @@ -269,8 +239,8 @@ Value Details In general, functions that starts with analyze* are expected to -work like rtables::analyze(), while functions that starts with summarize* -are based upon rtables::summarize_row_groups(). The latter provides a +work like rtables::analyze(), while functions that starts with summarize* +are based upon rtables::summarize_row_groups(). The latter provides a value for each dividing split in the row and column space, but, being it bound to the fundamental splits, it is repeated by design in every page when pagination is involved. @@ -279,9 +249,9 @@ DetailsFunctions analyze_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_num_patients(): Statistics function which counts the number of unique patients, the corresponding percentage taken with respect to the total number of patients, and the number of non-unique patients. diff --git a/main/reference/summarize_patients_exposure_in_cols.html b/main/reference/summarize_patients_exposure_in_cols.html index ffe89cefda..3cab97375f 100644 --- a/main/reference/summarize_patients_exposure_in_cols.html +++ b/main/reference/summarize_patients_exposure_in_cols.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -234,7 +204,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -252,32 +222,32 @@ ArgumentsValue analyze_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted data rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout. summarize_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout. s_count_patients_sum_exposure() returns a named list with the statistics:n_patients: Number of unique patients in df. sum_exposure: Sum of ex_var across all patients in df. -a_count_patients_sum_exposure() returns formatted rtables::CellValue(). +a_count_patients_sum_exposure() returns formatted rtables::CellValue(). Functions analyze_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::analyze_colvars(). +rtables::split_cols_by_multivar() and rtables::analyze_colvars(). summarize_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::summarize_row_groups(). +rtables::split_cols_by_multivar() and rtables::summarize_row_groups(). s_count_patients_sum_exposure(): Statistics function which counts numbers of patients and the sum of exposure across all patients. a_count_patients_sum_exposure(): Analysis function which is used as afun in -rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in -rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). +rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in +rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). Note diff --git a/main/reference/survival_biomarkers_subgroups.html b/main/reference/survival_biomarkers_subgroups.html index 91a000d62d..3e83a8ea13 100644 --- a/main/reference/survival_biomarkers_subgroups.html +++ b/main/reference/survival_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/survival_coxph_pairwise.html b/main/reference/survival_coxph_pairwise.html index 5112ec55c3..30bac141b4 100644 --- a/main/reference/survival_coxph_pairwise.html +++ b/main/reference/survival_coxph_pairwise.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -235,7 +205,7 @@ ArgumentsValue coxph_pairwise() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_coxph_pairwise() to the table layout. s_coxph_pairwise() returns the statistics:pvalue: p-value to test the null hypothesis that hazard ratio = 1. hr: Hazard ratio. @@ -243,13 +213,13 @@ Valuen_tot: Total number of observations. n_tot_events: Total number of events. -a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue(). +a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue(). Functions coxph_pairwise(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_coxph_pairwise(): Statistics function which analyzes HR, CIs of HR, and p-value of a Cox-PH model. a_coxph_pairwise(): Formatted analysis function which is used as afun in coxph_pairwise(). diff --git a/main/reference/survival_duration_subgroups.html b/main/reference/survival_duration_subgroups.html index cd75957453..6b1da60bf3 100644 --- a/main/reference/survival_duration_subgroups.html +++ b/main/reference/survival_duration_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -179,7 +149,7 @@ Arguments Value An rtables table summarizing survival by subgroup. -a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -190,8 +160,8 @@ DetailsFunctions tabulate_survival_subgroups(): Table-creating function which creates a table -summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_survival_subgroups(): Formatted analysis function which is used as afun in tabulate_survival_subgroups(). diff --git a/main/reference/survival_time.html b/main/reference/survival_time.html index 0c035af7aa..218c910f45 100644 --- a/main/reference/survival_time.html +++ b/main/reference/survival_time.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,7 +187,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -229,7 +199,7 @@ ArgumentsValue surv_time() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_time() to the table layout. s_surv_time() returns the statistics:median: Median survival time. median_ci: Confidence interval for median time. @@ -238,13 +208,13 @@ Valuerange_event: Survival time range for observations with events. range: Survival time range for all observations. -a_surv_time() returns the corresponding list with formatted rtables::CellValue(). +a_surv_time() returns the corresponding list with formatted rtables::CellValue(). Functions surv_time(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_surv_time(): Statistics function which analyzes survival times. a_surv_time(): Formatted analysis function which is used as afun in surv_time(). diff --git a/main/reference/survival_timepoint.html b/main/reference/survival_timepoint.html index 8aaa066601..ec25ceb866 100644 --- a/main/reference/survival_timepoint.html +++ b/main/reference/survival_timepoint.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -262,7 +232,7 @@ ArgumentsValue surv_timepoint() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_timepoint() and/or s_surv_timepoint_diff() to the table layout depending on the value of method. s_surv_timepoint() returns the statistics:pt_at_risk: Patients remaining at risk. @@ -270,18 +240,18 @@ Valuerate_se: Standard error of event free rate. rate_ci: Confidence interval for event free rate. -a_surv_timepoint() returns the corresponding list with formatted rtables::CellValue(). +a_surv_timepoint() returns the corresponding list with formatted rtables::CellValue(). s_surv_timepoint_diff() returns the statistics:rate_diff: Event-free rate difference between two groups. rate_diff_ci: Confidence interval for the difference. ztest_pval: p-value to test the difference is 0. -a_surv_timepoint_diff() returns the corresponding list with formatted rtables::CellValue(). +a_surv_timepoint_diff() returns the corresponding list with formatted rtables::CellValue(). Functions surv_timepoint(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_surv_timepoint(): Statistics function which analyzes survival rate. a_surv_timepoint(): Formatted analysis function which is used as afun in surv_timepoint() when method = "surv". diff --git a/main/reference/tern-package.html b/main/reference/tern-package.html index 786dc13bae..f874f57a46 100644 --- a/main/reference/tern-package.html +++ b/main/reference/tern-package.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy.glm.html b/main/reference/tidy.glm.html index 8ba67828d5..f38573bb34 100644 --- a/main/reference/tidy.glm.html +++ b/main/reference/tidy.glm.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy.step.html b/main/reference/tidy.step.html index 3102bfd85c..62d83532eb 100644 --- a/main/reference/tidy.step.html +++ b/main/reference/tidy.step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy_coxreg.html b/main/reference/tidy_coxreg.html index b5f23d9fb1..8c582bd258 100644 --- a/main/reference/tidy_coxreg.html +++ b/main/reference/tidy_coxreg.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/to_n.html b/main/reference/to_n.html index 7f5e1974e8..251d0c8988 100644 --- a/main/reference/to_n.html +++ b/main/reference/to_n.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/to_string_matrix.html b/main/reference/to_string_matrix.html index f89d568b37..626812db9c 100644 --- a/main/reference/to_string_matrix.html +++ b/main/reference/to_string_matrix.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/try_car_anova.html b/main/reference/try_car_anova.html index c284bc2d74..4ed62e0aa7 100644 --- a/main/reference/try_car_anova.html +++ b/main/reference/try_car_anova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/ungroup_stats.html b/main/reference/ungroup_stats.html index 19b4f9085b..b4e4fcce37 100644 --- a/main/reference/ungroup_stats.html +++ b/main/reference/ungroup_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/univariate.html b/main/reference/univariate.html index 0f9eb3bf68..aebc9f7618 100644 --- a/main/reference/univariate.html +++ b/main/reference/univariate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/unlist_and_blank_na.html b/main/reference/unlist_and_blank_na.html index d567cadc7b..7b4e397835 100644 --- a/main/reference/unlist_and_blank_na.html +++ b/main/reference/unlist_and_blank_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/update_weights_strat_wilson.html b/main/reference/update_weights_strat_wilson.html index 9d916b424f..5b1ae9ab57 100644 --- a/main/reference/update_weights_strat_wilson.html +++ b/main/reference/update_weights_strat_wilson.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/utils_split_funs.html b/main/reference/utils_split_funs.html index 3346cc2b60..064b48c0b0 100644 --- a/main/reference/utils_split_funs.html +++ b/main/reference/utils_split_funs.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -88,10 +58,10 @@ Collection of useful functions that are expanding on the core list of functions -provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() +provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() for more information on how to make a custom split function. All these functions -work with rtables::split_rows_by() argument split_fun to modify the way the split -happens. For other split functions, consider consulting rtables::split_funcs. +work with rtables::split_rows_by() argument split_fun to modify the way the split +happens. For other split functions, consider consulting rtables::split_funcs. @@ -132,7 +102,7 @@ Functions See also - rtables::make_split_fun() + rtables::make_split_fun() diff --git a/main/search.json b/main/search.json index 370f686740..283494a4a4 100644 --- a/main/search.json +++ b/main/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution Guidelines","title":"Contribution Guidelines","text":"🙏 Thank taking time contribute! input deeply valued, whether issue, pull request, even feedback, regardless size, content scope.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of contents","title":"Contribution Guidelines","text":"👶 Getting started 📔 Code Conduct 🗃 License 📜 Issues 🚩 Pull requests 💻 Coding guidelines 🏆 Recognition model ❓ Questions","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Contribution Guidelines","text":"Please refer project documentation brief introduction. Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: _. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: __. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/tern/main/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"variable-class-conversion","dir":"Articles","previous_headings":"","what":"Variable Class Conversion","title":"Missing Values in Tern","text":"rtables requires split variables factors. try split variable isn’t, warning message appear. purposefully convert SEX variable character demonstrate happens try splitting rows variable. fix , df_explict_na convert factor resulting table generated.","code":"adsl <- tern_ex_adsl adsl$SEX <- as.character(adsl$SEX) vars <- c(\"AGE\", \"SEX\", \"RACE\", \"BMRKR1\") var_labels <- c( \"Age (yr)\", \"Sex\", \"Race\", \"Continous Level Biomarker 1\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%) #> Race #> n 69 73 58 200 #> ASIAN 38 (55.1%) 43 (58.9%) 29 (50%) 110 (55%) #> BLACK OR AFRICAN AMERICAN 15 (21.7%) 13 (17.8%) 12 (20.7%) 40 (20%) #> WHITE 11 (15.9%) 12 (16.4%) 11 (19%) 34 (17%) #> AMERICAN INDIAN OR ALASKA NATIVE 4 (5.8%) 3 (4.1%) 6 (10.3%) 13 (6.5%) #> MULTIPLE 1 (1.4%) 1 (1.4%) 0 2 (1%) #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (1.4%) 0 1 (0.5%) #> OTHER 0 0 0 0 #> UNKNOWN 0 0 0 0 #> Continous Level Biomarker 1 #> n 69 73 58 200 #> Mean (SD) 6.3 (3.6) 6.7 (3.5) 6.2 (3.3) 6.4 (3.5) #> Median 5.4 6.3 5.4 5.6 #> Min - Max 0.4 - 17.8 1.0 - 18.5 2.4 - 19.1 0.4 - 19.1"},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"including-missing-values-in-rtables","dir":"Articles","previous_headings":"","what":"Including Missing Values in rtables","title":"Missing Values in Tern","text":"purposefully convert M values NA SEX variable. running df_explicit_na NA values encoded included table. well, missing values included n count included denominator value calculating percent values. want Na values displayed table included n count denominator calculating percent values, use na_level argument.","code":"adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl) vars <- c(\"AGE\", \"SEX\") var_labels <- c( \"Age (yr)\", \"Sex\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 38 40 32 110 #> F 38 (100%) 40 (100%) 32 (100%) 110 (100%) #> M 0 0 0 0 adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\") result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ———————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 0 0 0 0 #> Missing Values 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"missing-values-in-numeric-variables","dir":"Articles","previous_headings":"","what":"Missing Values in Numeric Variables","title":"Missing Values in Tern","text":"Numeric variables missing values altered. means NA value numeric variable included summary statistics, included denominator value calculating percent values. make value less 30 missing AGE variable valued greater 30 included table .","code":"adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl) vars <- c(\"AGE\", \"SEX\") var_labels <- c( \"Age (yr)\", \"Sex\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 46 56 44 146 #> Mean (SD) 37.8 (5.2) 38.3 (6.3) 39.1 (5.9) 38.3 (5.8) #> Median 37.2 37.3 37.5 37.5 #> Min - Max 30.3 - 48.0 30.0 - 57.5 30.5 - 58.3 30.0 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tern-tabulation","dir":"Articles","previous_headings":"","what":"tern Tabulation","title":"Tabulation","text":"tern R package provides functions create common analyses clinical trials R. core functionality tabulation built general purpose rtables package. New users first begin reading “Introduction tern” “Introduction rtables” vignettes. packages used vignette : datasets used vignette :","code":"library(rtables) library(tern) library(dplyr) adsl <- ex_adsl adae <- ex_adae adrs <- ex_adrs"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tern-analyze-functions","dir":"Articles","previous_headings":"","what":"tern Analyze Functions","title":"Tabulation","text":"Analyze functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analyze functions wrappers around rtables::analyze function, offer various methods useful perspective clinical trials statistical projects. Examples tern analyze functions count_occurrences, summarize_ancova analyze_vars. one prefix identify tern analyze functions recommended use tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"internals-of-tern-analyze-functions","dir":"Articles","previous_headings":"tern Analyze Functions","what":"Internals of tern Analyze Functions","title":"Tabulation","text":"Please skip subsection interested internals tern analyze functions. Internally tern analyze functions like summarize_ancova mainly built 4 elements chain: descriptions function type: analysis helper functions h_*. functions useful help define analysis. statistics function s_*. Statistics functions computation numbers tabulated later. order separate computation formatting, take care rcell type formatting . formatted analysis functions a_*. arguments corresponding statistics functions, can customized calling rtables::make_afun() . used afun rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions used combination rtables layout functions, pipeline creates table. last element chain. use native rtables::analyze function tern formatted analysis functions afun parameter. rtables::make_afun function helpful somebody wants attach format formatted analysis function.","code":"h_ancova() -> tern:::s_ancova() -> tern:::a_ancova() -> summarize_ancova() l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze(vars = \"AVAL\", afun = a_summary) build_table(l, df = adrs) afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = \"xx.\"), .labels = c(median = \"My median\"), .indent_mods = c(median = 1L) ) l2 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze(vars = \"AVAL\", afun = afun) build_table(l2, df = adrs)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tabulation-examples","dir":"Articles","previous_headings":"","what":"Tabulation Examples","title":"Tabulation","text":"going create 3 different tables using tern analyze functions rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"demographic-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Demographic Table","title":"Tabulation","text":"Demographic tables provide summary characteristics patients enrolled clinical trial. Typically table columns represent treatment arms variables summarized table demographic properties age, sex, race, etc. example function tern analyze_vars() remaining layout functions rtables. change display order categorical variables table use factor variables explicitly set order levels. case display order columns rows. Note forcats package many useful functions help types data processing steps (used ). tern package includes many functions similar analyze_vars(). functions called layout creating functions used combination rtables layout functions just like examples . Layout creating functions wrapping calls rtables analyze(), analyze_colvars() summarize_row_groups() provide options easy formatting analysis modifications. customize display demographics table, can via arguments analyze_vars(). layout creating functions tern include standard arguments .stats, .formats, .labels .indent_mods control statistics displayed numbers formatted. Refer package help help(\"analyze_vars\") ?analyze_vars see full set options. example change default summary numeric variables include number records, mean standard deviation (single statistic, .e. within single cell). categorical variables modify summary include number records counts categories. also modify display format mean standard deviation print two decimal places instead just one. One feature layout can used different datasets create different summaries. example, can easily create summary demographics Brazil China subgroups, respectively:","code":"# Select variables to include in table. vars <- c(\"AGE\", \"SEX\") var_labels <- c(\"Age (yr)\", \"Sex\") basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7) 34.9 (7.4) #> Median 33.0 35.0 35.0 34.0 #> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0 20.0 - 69.0 #> Sex #> n 134 134 132 400 #> F 79 (59%) 77 (57.5%) 66 (50%) 222 (55.5%) #> M 51 (38.1%) 55 (41%) 60 (45.5%) 166 (41.5%) #> U 3 (2.2%) 2 (1.5%) 4 (3%) 9 (2.2%) #> UNDIFFERENTIATED 1 (0.7%) 0 2 (1.5%) 3 (0.8%) # Reorder the levels in the ARM variable. adsl$ARM <- factor(adsl$ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\")) # Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c(\"M\", \"F\", \"U\", \"UNDIFFERENTIATED\")) basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 35.4 (7.9) 33.8 (6.6) 35.4 (7.7) 34.9 (7.4) #> Median 35.0 33.0 35.0 34.0 #> Min - Max 21.0 - 62.0 21.0 - 50.0 20.0 - 69.0 20.0 - 69.0 #> Sex #> n 134 134 132 400 #> M 55 (41%) 51 (38.1%) 60 (45.5%) 166 (41.5%) #> F 77 (57.5%) 79 (59%) 66 (50%) 222 (55.5%) #> U 2 (1.5%) 3 (2.2%) 4 (3%) 9 (2.2%) #> UNDIFFERENTIATED 0 1 (0.7%) 2 (1.5%) 3 (0.8%) # Select statistics and modify default formats. basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels, .stats = c(\"n\", \"mean_sd\", \"count\"), .formats = c(mean_sd = \"xx.xx (xx.xx)\") ) %>% build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ———————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 35.43 (7.90) 33.77 (6.55) 35.43 (7.72) 34.88 (7.44) #> Sex #> n 134 134 132 400 #> M 55 51 60 166 #> F 77 79 66 222 #> U 2 3 4 9 #> UNDIFFERENTIATED 0 1 2 3 lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"BRA\")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 7 13 10 30 #> Mean (SD) 32.0 (6.1) 36.7 (6.4) 38.3 (10.6) 36.1 (8.1) #> Median 32.0 37.0 35.0 35.5 #> Min - Max 25.0 - 42.0 24.0 - 47.0 25.0 - 64.0 24.0 - 64.0 #> Sex #> n 7 13 10 30 #> M 4 (57.1%) 8 (61.5%) 5 (50%) 17 (56.7%) #> F 3 (42.9%) 5 (38.5%) 5 (50%) 13 (43.3%) #> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"CHN\")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 81 74 64 219 #> Mean (SD) 35.7 (7.3) 33.0 (6.4) 35.2 (6.4) 34.6 (6.8) #> Median 36.0 32.0 35.0 34.0 #> Min - Max 21.0 - 58.0 23.0 - 48.0 21.0 - 49.0 21.0 - 58.0 #> Sex #> n 81 74 64 219 #> M 35 (43.2%) 27 (36.5%) 30 (46.9%) 92 (42%) #> F 45 (55.6%) 44 (59.5%) 29 (45.3%) 118 (53.9%) #> U 1 (1.2%) 2 (2.7%) 3 (4.7%) 6 (2.7%) #> UNDIFFERENTIATED 0 1 (1.4%) 2 (3.1%) 3 (1.4%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"adverse-event-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Adverse Event Table","title":"Tabulation","text":"standard table adverse events summary system organ class preferred term. frequency counts preferred term, multiple occurrences AE individual count . create table need use combination several layout creating functions tabulation pipeline. start creating high-level summary. layout creating function tern can summarize_num_patients(): Note table, denominator used percentages shown header table (N = xx) defined based subject-level dataset adsl. done using alt_df_counts argument build_table(), provides alternative data set deriving counts header. often required work data sets include multiple records per patient df, adae .","code":"basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"statistics-functions","dir":"Articles","previous_headings":"Tabulation Examples > Adverse Event Table","what":"Statistics Functions","title":"Tabulation","text":"building rest AE table helpful introduce tern package design conventions. layout creating function tern wrapper Statistics function. Statistics functions ones actual computation numbers table. functions always return named lists whose elements statistics available include layout via .stats argument layout creating function level. Statistics functions follow naming convention always begin s_* ease use documented page layout creating function counterpart. helpful review Statistic function understand logic used calculate numbers table see options may available modify analysis. example, Statistics function calculating numbers summarize_num_patients() s_num_patients(). results Statistics function list elements unique, nonunique unique_count: results can see unique nonunique statistics displayed “Patients” column initial AE table output . Also can see raw numbers formatted way. formatting functionality handled layout creating function level .formats argument. Now know types statistics can derived s_num_patients(), can try modifying default layout returned summarize_num_patients(). Instead reporting unique nonqunie statistics, specify analysis include unique_count statistic. result show counts unique patients. Note make update .stats .labels argument summarize_num_patients(). Let’s now continue building layout adverse event table. top-level summary, can repeat summary system organ class level. split analysis data split_rows_by() calling summarize_num_patients(). table looks almost ready. final step, need layout creating function can produce count table event frequencies. layout creating function count_occurrences(). Let’s first try using function simpler layout without row splits: Putting everything together, final AE table looks like :","code":"s_num_patients(x = adae$USUBJID, labelstr = \"\", .N_col = nrow(adae)) #> $unique #> [1] 365.000000 0.188728 #> attr(,\"label\") #> [1] \"\" #> #> $nonunique #> [1] 1934 #> attr(,\"label\") #> [1] \"\" #> #> $unique_count #> [1] 365 #> attr(,\"label\") #> [1] \"(n)\" basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\", .labels = c(unique_count = \"Total number of patients with at least one AE\") ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 123 120 365 basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% split_rows_by( \"AEBODSYS\", child_labels = \"visible\", nested = FALSE, indent_mod = -1L, split_fun = drop_split_levels ) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934 #> cl A.1 #> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%) #> Overall total number of events 132 130 160 422 #> cl B.1 #> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> Overall total number of events 56 60 62 178 #> cl B.2 #> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%) #> Overall total number of events 129 138 143 410 #> cl C.1 #> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> Overall total number of events 55 63 64 182 #> cl C.2 #> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> Overall total number of events 48 53 65 166 #> cl D.1 #> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%) #> Overall total number of events 127 106 135 368 #> cl D.2 #> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) #> Overall total number of events 62 72 74 208 basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% count_occurrences(vars = \"AEDECOD\") %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————— #> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%) #> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%) #> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%) #> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%) #> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%) #> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%) #> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% split_rows_by( \"AEBODSYS\", child_labels = \"visible\", nested = FALSE, indent_mod = -1L, split_fun = drop_split_levels ) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% count_occurrences(vars = \"AEDECOD\") %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934 #> cl A.1 #> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%) #> Overall total number of events 132 130 160 422 #> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%) #> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%) #> cl B.1 #> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> Overall total number of events 56 60 62 178 #> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> cl B.2 #> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%) #> Overall total number of events 129 138 143 410 #> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%) #> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%) #> cl C.1 #> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> Overall total number of events 55 63 64 182 #> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> cl C.2 #> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> Overall total number of events 48 53 65 166 #> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> cl D.1 #> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%) #> Overall total number of events 127 106 135 368 #> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%) #> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%) #> cl D.2 #> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) #> Overall total number of events 62 72 74 208 #> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"response-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Response Table","title":"Tabulation","text":"typical response table binary clinical trial endpoint may composed several different analyses: Proportion responders treatment group Difference proportion responders comparison groups vs. control group Chi-Square test difference response rates comparison groups vs. control group can build table layout like following approach used AE table: table section produced using different layout creating function tern. First start data preparation steps set analysis dataset. select endpoint analyze PARAMCD define logical variable is_rsp indicates whether patient classified responder . create summary proportion responders treatment group, use estimate_proportion() layout creating function: specify arm table used reference, use argument ref_group split_cols_by(). change reference arm “B: Placebo” arm displayed first column: customize analysis, can use method conf_level arguments modify type confidence interval calculated: next table section needed summarize difference response rates reference arm comparison arm. Use estimate_proportion_diff() layout creating function : final section needed complete table includes statistical test difference response rates. Use test_proportion_diff() layout creating function : customize output, use method argument select Chi-Squared test Schouten correction. Now can put table sections together one layout pipeline. Note one small change needed. Since primary analysis variable table sections (is_rsp), need give sub-table unique name. done adding table_names argument providing unique names :","code":"# Preprocessing to select an analysis endpoint. anl <- adrs %>% dplyr::filter(PARAMCD == \"BESRSPI\") %>% dplyr::mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) basic_table() %>% split_cols_by(var = \"ARM\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", table_names = \"est_prop\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", method = \"clopper-pearson\", conf_level = 0.9 ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion_diff( vars = \"is_rsp\", show_labels = \"visible\", var_labels = \"Unstratified Analysis\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— #> Unstratified Analysis #> Difference in Response rate (%) 17.9 23.7 #> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% test_proportion_diff(vars = \"is_rsp\") %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— #> p-value (Chi-Squared Test) 0.0006 <0.0001 basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% test_proportion_diff( vars = \"is_rsp\", method = \"schouten\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001 basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", method = \"clopper-pearson\", conf_level = 0.9, table_names = \"est_prop\" ) %>% estimate_proportion_diff( vars = \"is_rsp\", show_labels = \"visible\", var_labels = \"Unstratified Analysis\", table_names = \"est_prop_diff\" ) %>% test_proportion_diff( vars = \"is_rsp\", method = \"schouten\", table_names = \"test_prop_diff\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7) #> Unstratified Analysis #> Difference in Response rate (%) 17.9 23.7 #> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8) #> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Tabulation","text":"Tabulation tern builds top layout tabulation framework rtables. Complex tables built step step pipeline combining layout creating functions perform specific type analysis. tern analyze functions introduced vignette : analyze_vars() summarize_num_patients() count_occurrences() estimate_proportion() estimate_proportion_diff() test_proportion_diff() Layout creating functions build formatted layout controlling features labels, numerical display formats indentation. functions wrappers Statistics functions calculate raw summaries analysis. can easily spot Statistics functions documentation always begin prefix s_. can helpful inspect run Statistics functions understand ways analysis can customized.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"introduction-to-tern","dir":"Articles","previous_headings":"","what":"Introduction to tern","title":"Introduction to tern","text":"vignette shows general purpose syntax tern R package. tern R package contains analytical functions creating tables graphs useful clinical trials statistical analysis. main focus clinical trial reporting tables graphs related clinical trials also valuable. core functionality tabulation built top general purpose rtables package. strongly recommended start reading “Introduction rtables” vignette get familiar concept rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"common-clinical-trials-analyses","dir":"Articles","previous_headings":"","what":"Common Clinical Trials Analyses","title":"Introduction to tern","text":"package provides large range functionality create tables graphs used clinical trial statistical analysis. rtables tabulation extended clinical trials specific functions: demographics unique patients exposure across patients change baseline parameters statistical model fits: MMRM, logistic regression, Cox regression, … … rtables tabulation helper functions: pre-processing conversions transformations … data visualizations connected clinical trials: Kaplan-Meier plots forest plots line plots … data visualizations helper functions: arrange/stack multiple graphs embellishing graphs/tables metadata details, adding titles, footnotes, page number, etc. … reference tern functions available tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"analytical-functions-for-rtables","dir":"Articles","previous_headings":"","what":"Analytical Functions for rtables","title":"Introduction to tern","text":"Analytical functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analytical functions wrappers around rtables::analyze function; offer various methods useful perspective clinical trials statistical projects. Examples tern analytical functions count_occurrences, summarize_ancova analyze_vars. one prefix identify tern analytical functions recommended use reference subsection tern website. rtables code first describe two tables assign descriptions variables lyt lyt2. built tables using actual data rtables::build_table. description table called table layout. analyze instruction adds layout ARM variable analyzed mean analysis function result rounded 1 decimal place. Hence, layout “pre-data”; , ’s description build table get data. Defining table layout pure rtables code. tern function analyze_vars replaces rtables::analyze function . see tern offers advanced analysis extending rtables function calls one additional function call. examples tabulation analyze functions presented Tabulation vignette.","code":"library(tern) library(dplyr) # Create table layout pure rtables lyt <- rtables::basic_table() %>% rtables::split_cols_by(var = \"ARM\") %>% rtables::split_rows_by(var = \"AVISIT\") %>% rtables::analyze(vars = \"AVAL\", mean, format = \"xx.x\") # Create table layout with tern analyze_vars analyze function lyt2 <- rtables::basic_table() %>% rtables::split_cols_by(var = \"ARM\") %>% rtables::split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\", .formats = c(\"mean_sd\" = \"(xx.xx, xx.xx)\")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING #> mean 3.0 3.0 3.0 #> BASELINE #> mean 2.5 2.8 2.5 #> END OF INDUCTION #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING #> n 154 178 144 #> Mean (SD) (3.00, 0.00) (3.00, 0.00) (3.00, 0.00) #> Median 3.0 3.0 3.0 #> Min - Max 3.0 - 3.0 3.0 - 3.0 3.0 - 3.0 #> BASELINE #> n 136 146 124 #> Mean (SD) (2.46, 0.88) (2.77, 1.00) (2.46, 1.08) #> Median 3.0 3.0 3.0 #> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0 #> END OF INDUCTION #> n 218 205 217 #> Mean (SD) (1.75, 0.90) (2.14, 1.28) (1.65, 1.06) #> Median 2.0 2.0 1.0 #> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0 #> FOLLOW UP #> n 164 153 167 #> Mean (SD) (2.23, 1.26) (2.89, 1.29) (1.97, 1.01) #> Median 2.0 4.0 2.0 #> Min - Max 1.0 - 4.0 1.0 - 4.0 1.0 - 4.0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"clinical-trials-visualizations","dir":"Articles","previous_headings":"","what":"Clinical Trials Visualizations","title":"Introduction to tern","text":"Clinical trial related plots complement rich palette tern tabulation analysis functions. Thus tern package delivers full-featured tool clinical trial reporting. tern plot functions return ggplot2 gTree objects, latter returned table attached plot. optional nestcolor package can loaded apply standardized NEST color palette tern plots. Line plot without table generated g_lineplot function. Line plot table generated g_lineplot function. first plot ggplot2 object second plot gTree object, latter contains table. second plot properly resized get clear readable table content. tern functions used plot generation mostly g_ prefixed. tern plot functions listed tern website functions reference.","code":"adsl <- formatters::ex_adsl adlb <- formatters::ex_adlb adlb <- dplyr::filter(adlb, PARAMCD == \"ALT\", AVISIT != \"SCREENING\") library(nestcolor) # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, table and customized confidence level g_lineplot( adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"), title = \"Plot of Mean and 80% Confidence Limits by Visit\" )"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"interactive-apps","dir":"Articles","previous_headings":"","what":"Interactive Apps","title":"Introduction to tern","text":"tern outputs easily accommodated shiny apps. recommend applying tern outputs teal apps. teal package shiny-based interactive exploration framework analyzing data. teal shiny apps tern outputs available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to tern","text":"summary, tern contains many additional functions creating tables, listing graphs used clinical trials statistical analyses. design package gives users lot flexibility meet analysis needs regulatory exploratory reporting context. information please explore tern website.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"tern-formatting-functions-overview","dir":"Articles","previous_headings":"","what":"tern Formatting Functions Overview","title":"Formatting Functions","text":"tern R package provides functions create common analyses clinical trials R functions default formatting arguments displaying values output specific way. tern formatting differs compared formatting available formatters package tern formats capable handling logical statements, allowing fine-tuning output displayed. Depending type value displayed, value , format output change. Whereas using formatters package, specified format applied regardless value. see available formatting functions available tern see ?formatting_functions. see available format strings available formatters see formatters::list_valid_format_labels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"comparing-tern-formatters-formats","dir":"Articles","previous_headings":"","what":"Comparing tern & formatters Formats","title":"Formatting Functions","text":"packages used vignette : example demonstrates use tern formatting count_abnormal() function. example “low” category non-zero numerator value fraction percentage value displayed, “high” value numerator value zero fraction value displayed without also displaying redundant zero percentage value. following example count_abnormal() function utilized . time “low” values “high” values non-zero numerator show percentage. following example demonstrates difference formatters used instead format output. choose use \"xx / xx\" value format. “high” value zero numerator value “low” value non-zero numerator, yet displayed format. concept occurs using available formats formatters package. following example displays result using \"xx.x / xx.x\" format instead. Use formatters::list_valid_format_labels() see full list available formats formatters.","code":"library(rtables) library(formatters) library(tern) library(dplyr) df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) #> high 0/2 df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) #> high 1/2 (50%) df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = \"xx / xx\") ) %>% build_table(df2) #> all obs #> —————————————— #> low 2 / 2 #> high 0 / 2 df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = \"xx.x / xx.x\") ) %>% build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 #> high 0.0 / 2.0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"formatting-function-basics","dir":"Articles","previous_headings":"","what":"Formatting Function Basics","title":"Formatting Functions","text":"Current tern formatting functions consider following aspects setting custom behaviors: Missing values - custom value string can set display missing values instead NA. 0’s - cell value zero, tern fraction formatting functions exclude accompanying percentage value. Number decimal places display - number decimal places can fixed needed. Value thresholds - different format value can displayed depending whether value within certain threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"number-of-decimal-places-to-display","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Number of Decimal Places to Display","title":"Formatting Functions","text":"Two functions set fixed number decimal places (specifically 1) format_fraction_fixed_dp() format_count_fraction_fixed_dp(). default, formatting functions remove trailing zeros, two functions always one decimal place percentage, even digit zero. See following example:","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 3L)) #> [1] \"1/3 (33.3%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.25)) #> [1] \"2 (25.0%)\""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"value-thresholds","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Value Thresholds","title":"Formatting Functions","text":"Functions set custom values according certain threshold include format_extreme_values(), format_extreme_values_ci(), format_fraction_threshold(). extreme value formats work similarly allow user specify maximum number digits include, large small values given special string value. example: format_fraction_threshold() function allows user specify lower percentage threshold, values instead assigned special string value. example: See documentation function specific details behavior customize .","code":"extreme_format <- format_extreme_values(digits = 2) extreme_format(0.235) #> [1] \"0.23\" extreme_format(0.001) #> [1] \"<0.01\" extreme_format(Inf) #> [1] \">999.99\" fraction_format <- format_fraction_threshold(0.05) fraction_format(x = c(20, 0.1)) #> [1] 10 fraction_format(x = c(2, 0.01)) #> [1] \"<5\""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"creating-custom-formatting-functions","dir":"Articles","previous_headings":"","what":"Creating Custom Formatting Functions","title":"Formatting Functions","text":"table requires customized output displayed using one pre-existing tern formatting functions, may want consider creating new formatting function. creating formatting function important consider aspects listed Formatting Function Customization section . section create custom formatting function derived format_fraction_fixed_dp() function. First take look function detail customize . see numerator value greater 0, fraction percentage displayed. numerator 0, fraction shown. Percent values always display 1 decimal place. create dummy dataset observe output value behavior formatting function applied. Now modify function make custom formatting function, custom_format. want display 3 decimal places percent value, numerator value 0 want display 0 value (without denominator).","code":"# First we will see how the format_fraction_fixed_dp code works and displays the outputs format_fraction_fixed_dp <- function(x, ...) { attr(x, \"label\") <- NULL checkmate::assert_vector(x) checkmate::assert_count(x[\"num\"]) checkmate::assert_count(x[\"denom\"]) result <- if (x[\"num\"] == 0) { paste0(x[\"num\"], \"/\", x[\"denom\"]) } else { paste0( x[\"num\"], \"/\", x[\"denom\"], \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" ) } return(result) } df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) #> high 0/2 custom_format <- function(x, ...) { attr(x, \"label\") <- NULL checkmate::assert_vector(x) checkmate::assert_count(x[\"num\"]) checkmate::assert_count(x[\"denom\"]) result <- if (x[\"num\"] == 0) { paste0(x[\"num\"]) # We remove the denominator on this line so that only a 0 is displayed } else { paste0( x[\"num\"], \"/\", x[\"denom\"], \" (\", sprintf(\"%.3f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" # We include 3 decimal places with %.3f ) } return(result) } basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) #> high 0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Formatting Functions","text":"tern analysis function pre-specified default format functions implement generating output, taken formatters package custom formatting functions stored tern. tern functions differ compared formatters logical statements can used set value-dependent customized formats. like create custom formatting function use tern, sure carefully consider rules want implement handle different input values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joe Zhu. Author, maintainer. Daniel Sabanés Bové. Author. Jana Stoilova. Author. Davide Garolini. Author. Emily de la Rua. Author. Abinaya Yogasekaram. Author. Heng Wang. Author. Francois Collin. Author. Adrian Waddell. Author. Pawel Rucki. Author. Chendi Liao. Author. Jennifer Li. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/tern/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram , Wang H, Collin F, Waddell , Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used Clinical Trials. R package version 0.9.5.9026, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/main/.","code":"@Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, note = {R package version 0.9.5.9026, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/main/}, }"},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"tern-","dir":"","previous_headings":"","what":"Create Common TLGs Used in Clinical Trials","title":"Create Common TLGs Used in Clinical Trials","text":"tern R package contains analysis functions create tables graphs used clinical trial reporting. package provides large range functionality, : Data visualizations: Line plots (g_lineplot) Kaplan-Meier plots (g_km) Forest plots (g_forest) STEP graphs (g_step) Individual patient plots (g_ipp) Waterfall plots (g_waterfall) Statistical model fit summaries: Logistic regression (summarize_logistic) Cox regression (summarize_coxreg) Analysis tables: See list available analyze functions See list available summarize functions See list available column-wise analysis functions Many outputs available added teal shiny applications interactive exploration data. teal modules available teal.modules.clinical package. See TLG Catalog extensive catalog example clinical trial tables, listings, graphs created using tern functionality.","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Create Common TLGs Used in Clinical Trials","text":"tern available CRAN can install latest released version : can install latest development version directly GitHub running following:","code":"install.packages(\"tern\") # install.packages(\"pak\") pak::pak(\"insightsengineering/tern\")"},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Create Common TLGs Used in Clinical Trials","text":"understand use package, please refer Introduction tern article, provides multiple examples code implementation. See package vignettes browseVignettes(package = \"tern\") usage package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"related","dir":"","previous_headings":"","what":"Related","title":"Create Common TLGs Used in Clinical Trials","text":"rtables - table engine used tlg-catalog - website showcasing many examples clinical trial tables, listings, graphs teal.modules.clinical - teal modules interactive data analysis","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"Create Common TLGs Used in Clinical Trials","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with abnormal range values — abnormal","title":"Count patients with abnormal range values — abnormal","text":"analyze function count_abnormal() creates layout element count patients abnormal analysis range values direction. function analyzes primary analysis variable var indicates abnormal range results. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline (defaults BNRIND), variable indicate baseline reference ranges. direction specified via abnormal parameter (e.g. High Low), fraction patient counts returned, numerator denominator calculated follows: num: number patients abnormality recorded treatment. denom: total number patients least one post-baseline assessment. function assumes df filtered include post-baseline records.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with abnormal range values — abnormal","text":"","code":"count_abnormal( lyt, var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = var, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal( df, .var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE ) a_count_abnormal( df, .var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with abnormal range values — abnormal","text":"lyt (PreDataTableLayouts) layout analyses added . abnormal (named list) list identifying abnormal range level(s) var. Defaults list(Low = \"LOW\", High = \"HIGH\") can also group different levels named list, example, abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")). variables (named list string) list additional analysis variables. exclude_base_abn (flag) whether exclude subjects baseline abnormality numerator denominator. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal() table layout. s_count_abnormal() returns statistic fraction vector num denom counts patients. a_count_abnormal() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal(): Statistics function counts patients abnormal range values single abnormal level. a_count_abnormal(): Formatted analysis function used afun count_abnormal().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal() considers single variable contains multiple abnormal levels. df filtered include post-baseline records. denominator includes patients may abnormal levels baseline, patients missing baseline records. Patients abnormalities baseline can optionally excluded numerator denominator via exclude_base_abn parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with abnormal range values — abnormal","text":"","code":"library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #> #> matches #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union df <- data.frame( USUBJID = as.character(c(1, 1, 2, 2)), ANRIND = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BNRIND = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") # Layout creating function. basic_table() %>% count_abnormal(var = \"ANRIND\", abnormal = list(high = \"HIGH\", low = \"LOW\")) %>% build_table(df) #> all obs #> ———————————————— #> high 1/2 (50%) #> low 1/2 (50%) # Passing of statistics function and formatting arguments. df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) # Select only post-baseline records. df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\") ) %>% build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) #> high 1/2 (50%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"analyze function count_abnormal_by_baseline() creates layout element count patients abnormal analysis range values, categorized baseline status. function analyzes primary analysis variable var indicates abnormal range results. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline (defaults BNRIND), variable indicate baseline reference ranges. direction specified via abnormal parameter (e.g. High Low), condition baseline range result count patients numerator denominator follows following categories: num: number patients without abnormality baseline (excluding missing baseline) least one abnormality post-baseline. denom: number patients without abnormality baseline (excluding missing baseline). num: number patients abnormality baseline least one abnormality post-baseline. denom: number patients abnormality baseline. Total num: number patients least one post-baseline record least one abnormality post-baseline. denom: number patients least one post-baseline record. function assumes df filtered include post-baseline records.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"","code":"count_abnormal_by_baseline( lyt, var, abnormal, variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), na_str = \"\", nested = TRUE, ..., table_names = abnormal, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_baseline( df, .var, abnormal, na_str = \"\", variables = list(id = \"USUBJID\", baseline = \"BNRIND\") ) a_count_abnormal_by_baseline( df, .var, abnormal, na_str = \"\", variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"lyt (PreDataTableLayouts) layout analyses added . abnormal (character) values identifying abnormal range level(s) .var. variables (named list string) list additional analysis variables. na_str (string) explicit na_level argument used pre-processing steps (maybe df_explicit_na()). default \"\". nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal_by_baseline\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"count_abnormal_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_baseline() table layout. s_count_abnormal_by_baseline() returns statistic fraction named list 3 labeled elements: not_abnormal, abnormal, total. element contains vector num denom patient counts. a_count_abnormal_by_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"count_abnormal_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function used afun count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"df filtered include post-baseline records. baseline variable analysis variable contains NA records, expected df pre-processed using df_explicit_na() explicit_na().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"","code":"df <- data.frame( USUBJID = as.character(c(1:6)), ANRIND = factor(c(rep(\"LOW\", 4), \"NORMAL\", \"HIGH\")), BNRIND = factor(c(\"LOW\", \"NORMAL\", \"HIGH\", NA, \"LOW\", \"NORMAL\")) ) df <- df_explicit_na(df) # Layout creating function. basic_table() %>% count_abnormal_by_baseline(var = \"ANRIND\", abnormal = c(High = \"HIGH\")) %>% build_table(df) #> all obs #> ———————————————————————— #> High #> Not high 1/4 (25%) #> High 0/1 #> Total 1/6 (16.7%) # Passing of statistics function and formatting arguments. df2 <- data.frame( ID = as.character(c(1, 2, 3, 4)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BLRANGE = factor(c(\"LOW\", \"HIGH\", \"HIGH\", \"NORMAL\")) ) basic_table() %>% count_abnormal_by_baseline( var = \"RANGE\", abnormal = c(Low = \"LOW\"), variables = list(id = \"ID\", baseline = \"BLRANGE\"), .formats = c(fraction = \"xx / xx\"), .indent_mods = c(fraction = 2L) ) %>% build_table(df2) #> all obs #> ——————————————————————— #> Low #> Not low 1 / 3 #> Low 0 / 1 #> Total 1 / 4"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with marked laboratory abnormalities — abnormal_by_marked","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"analyze function count_abnormal_by_marked() creates layout element count patients marked laboratory abnormalities direction abnormality, categorized parameter value. function analyzes primary analysis variable var indicates whether single, replicated, last marked laboratory abnormality observed. Levels var include marked lab abnormality (single last_replicated) can supplied via category parameter. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, param (defaults PARAM), variable indicate parameter values, direction (defaults abn_dir), variable indicate abnormality directions. combination param direction levels, marked lab abnormality counts calculated follows: Single, last & Last replicated: number patients Single, last Last replicated values, respectively. : number patients either single replicated marked abnormalities. Fractions calculated dividing counts number patients least one valid measurement recorded analysis. Prior using function table layout must use rtables::split_rows_by() create two row splits, one variable param one variable direction.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"","code":"count_abnormal_by_marked( lyt, var, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\"), na_str = default_na_str(), nested = TRUE, ..., .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_marked( df, .var = \"AVALCAT1\", .spl_context, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") ) a_count_abnormal_by_marked( df, .var = \"AVALCAT1\", .spl_context, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"lyt (PreDataTableLayouts) layout analyses added . category (list) list different marked category names single last replicated. variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"abnormal_by_marked\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"count_abnormal_by_marked() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_marked() table layout. s_count_abnormal_by_marked() returns statistic count_fraction Single, last, Last replicated, results. a_count_abnormal_by_marked() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"count_abnormal_by_marked(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_marked(): Statistics function patients marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function used afun count_abnormal_by_marked().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"Single, last Last replicated levels mutually exclusive. patient abnormalities meet Single, last Last replicated criteria, patient counted Last replicated category.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))), ARMCD = factor(c(rep(\"ARM A\", 5), rep(\"ARM B\", 5), rep(\"ARM A\", 5), rep(\"ARM B\", 5))), ANRIND = factor(c( \"NORMAL\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"HIGH\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"NORMAL\", \"HIGH HIGH\", \"NORMAL\", \"LOW\", \"LOW\", \"LOW LOW\", \"LOW\", \"LOW\", \"LOW\", \"LOW LOW\", \"NORMAL\", \"LOW LOW\" )), ONTRTFL = rep(c(\"\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\"), 2), PARAMCD = factor(c(rep(\"CRP\", 10), rep(\"ALT\", 10))), AVALCAT1 = factor(rep(c(\"\", \"\", \"\", \"SINGLE\", \"REPLICATED\", \"\", \"\", \"LAST\", \"\", \"SINGLE\"), 2)), stringsAsFactors = FALSE ) df <- df %>% mutate(abn_dir = factor( case_when( ANRIND == \"LOW LOW\" ~ \"Low\", ANRIND == \"HIGH HIGH\" ~ \"High\", TRUE ~ \"\" ), levels = c(\"Low\", \"High\") )) # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") df_crp <- df %>% filter(PARAMCD == \"CRP\") %>% droplevels() full_parent_df <- list(df_crp, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(df_crp)), \"not_needed\") spl_context <- data.frame( split = c(\"PARAMCD\", \"GRADE_DIR\"), full_parent_df = I(full_parent_df), cur_col_subset = I(cur_col_subset) ) map <- unique( df[df$abn_dir %in% c(\"Low\", \"High\") & df$AVALCAT1 != \"\", c(\"PARAMCD\", \"abn_dir\")] ) %>% lapply(as.character) %>% as.data.frame() %>% arrange(PARAMCD, abn_dir) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAMCD\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\" ) %>% split_rows_by( \"abn_dir\", split_fun = trim_levels_to_map(map) ) %>% count_abnormal_by_marked( var = \"AVALCAT1\", variables = list( id = \"USUBJID\", param = \"PARAMCD\", direction = \"abn_dir\" ) ) %>% build_table(df = df) #> ARM A ARM B #> ———————————————————————————————————————————— #> ALT (n) 1 1 #> Low #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) #> CRP (n) 1 1 #> High #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAMCD\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\" ) %>% split_rows_by( \"abn_dir\", split_fun = trim_levels_in_group(\"abn_dir\") ) %>% count_abnormal_by_marked( var = \"AVALCAT1\", variables = list( id = \"USUBJID\", param = \"PARAMCD\", direction = \"abn_dir\" ) ) %>% build_table(df = df) #> ARM A ARM B #> ———————————————————————————————————————————— #> ALT (n) 1 1 #> Low #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) #> CRP (n) 1 1 #> High #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"analyze function count_abnormal_by_worst_grade() creates layout element count patients highest (worst) analysis toxicity grade post-baseline direction, categorized parameter value. function analyzes primary analysis variable var indicates toxicity grades. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, param (defaults PARAM), variable indicate parameter values, grade_dir (defaults GRADE_DIR), variable indicate directions (e.g. High Low) toxicity grade supplied var. combination param grade_dir levels, patient counts worst grade calculated follows: 1 4: number patients worst grades 1-4, respectively. : number patients least one abnormality (.e. grade 0). Fractions calculated dividing counts number patients least one valid measurement recorded treatment. Pre-processing crucial using function can done automatically using h_adlb_abnormal_by_worst_grade() helper function. See description function details necessary pre-processing steps. Prior using function table layout must use rtables::split_rows_by() create two row splits, one variable param one variable grade_dir.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"","code":"count_abnormal_by_worst_grade( lyt, var, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\"), na_str = default_na_str(), nested = TRUE, ..., .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_worst_grade( df, .var = \"GRADE_ANL\", .spl_context, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") ) a_count_abnormal_by_worst_grade( df, .var = \"GRADE_ANL\", .spl_context, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"abnormal_by_worst_grade\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"count_abnormal_by_worst_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_worst_grade() table layout. s_count_abnormal_by_worst_grade() returns single statistic count_fraction grades 1 4 \"\" results. a_count_abnormal_by_worst_grade() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"count_abnormal_by_worst_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function counts patients worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function used afun count_abnormal_by_worst_grade().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"","code":"library(dplyr) library(forcats) adlb <- tern_ex_adlb # Data is modified in order to have some parameters with grades only in one direction # and simulate the real data. adlb$ATOXGR[adlb$PARAMCD == \"ALT\" & adlb$ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\")] <- \"-1\" adlb$ANRIND[adlb$PARAMCD == \"ALT\" & adlb$ANRIND == \"HIGH\"] <- \"LOW\" adlb$WGRHIFL[adlb$PARAMCD == \"ALT\"] <- \"\" adlb$ATOXGR[adlb$PARAMCD == \"IGA\" & adlb$ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\")] <- \"1\" adlb$ANRIND[adlb$PARAMCD == \"IGA\" & adlb$ANRIND == \"LOW\"] <- \"HIGH\" adlb$WGRLOFL[adlb$PARAMCD == \"IGA\"] <- \"\" # Pre-processing adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade() # Map excludes records without abnormal grade since they should not be displayed # in the table. map <- unique(adlb_f[adlb_f$GRADE_DIR != \"ZERO\", c(\"PARAM\", \"GRADE_DIR\", \"GRADE_ANL\")]) %>% lapply(as.character) %>% as.data.frame() %>% arrange(PARAM, desc(GRADE_DIR), GRADE_ANL) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAM\") %>% split_rows_by(\"GRADE_DIR\", split_fun = trim_levels_to_map(map)) %>% count_abnormal_by_worst_grade( var = \"GRADE_ANL\", variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") ) %>% build_table(df = adlb_f) #> ARM A ARM B ARM C #> ——————————————————————————————————————————————————————————————————————————— #> Alanine Aminotransferase Measurement #> LOW #> 1 12 (17.4%) 5 (6.8%) 8 (13.8%) #> 2 9 (13%) 13 (17.8%) 6 (10.3%) #> 3 6 (8.7%) 4 (5.5%) 6 (10.3%) #> 4 7 (10.1%) 7 (9.6%) 6 (10.3%) #> Any 34 (49.3%) 29 (39.7%) 26 (44.8%) #> C-Reactive Protein Measurement #> LOW #> 1 11 (15.9%) 12 (16.4%) 7 (12.1%) #> 2 8 (11.6%) 2 (2.7%) 6 (10.3%) #> 3 4 (5.8%) 9 (12.3%) 6 (10.3%) #> 4 7 (10.1%) 6 (8.2%) 4 (6.9%) #> Any 30 (43.5%) 29 (39.7%) 23 (39.7%) #> HIGH #> 1 8 (11.6%) 11 (15.1%) 2 (3.4%) #> 2 9 (13%) 11 (15.1%) 11 (19%) #> 3 14 (20.3%) 10 (13.7%) 5 (8.6%) #> 4 2 (2.9%) 4 (5.5%) 6 (10.3%) #> Any 33 (47.8%) 36 (49.3%) 24 (41.4%) #> Immunoglobulin A Measurement #> HIGH #> 1 7 (10.1%) 7 (9.6%) 6 (10.3%) #> 2 8 (11.6%) 6 (8.2%) 8 (13.8%) #> 3 7 (10.1%) 5 (6.8%) 9 (15.5%) #> 4 6 (8.7%) 2 (2.7%) 3 (5.2%) #> Any 28 (40.6%) 20 (27.4%) 26 (44.8%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"analyze function count_abnormal_lab_worsen_by_baseline() creates layout element count patients analysis toxicity grades worsened baseline, categorized highest (worst) grade post-baseline. function analyzes primary analysis variable var indicates analysis toxicity grades. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline_var (defaults BTOXGR), variable indicate baseline toxicity grades, direction_var (defaults GRADDIR), variable indicate toxicity grade directions interest include (e.g. \"H\" (high), \"L\" (low), \"B\" ()). direction(s) specified direction_var, patient counts worst grade patients worsened baseline calculated follows: 1 4: number patients worsened baseline grades worst grades 1-4, respectively. : total number patients worsened baseline grades. Fractions calculated dividing counts number patients analysis toxicity grades worsened baseline toxicity grades treatment. Prior using function table layout must use rtables::split_rows_by() create row split variable direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"","code":"count_abnormal_lab_worsen_by_baseline( lyt, var, variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\"), na_str = default_na_str(), nested = TRUE, ..., table_names = NULL, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_lab_worsen_by_baseline( df, .var = \"ATOXGR\", variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") ) a_count_abnormal_lab_worsen_by_baseline( df, .var = \"ATOXGR\", variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables including: id (string) subject variable name. baseline_var (string) name data column containing baseline toxicity variable. direction_var (string) see direction_var details. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal_by_worst_grade_worsen\") see available statistics. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"count_abnormal_lab_worsen_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_lab_worsen_by_baseline() table layout. s_count_abnormal_lab_worsen_by_baseline() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\". a_count_abnormal_lab_worsen_by_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"count_abnormal_lab_worsen_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function patients whose worst post-baseline lab grades worse baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function used afun count_abnormal_lab_worsen_by_baseline().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" ) basic_table() %>% split_cols_by(\"ARMCD\") %>% add_colcounts() %>% split_rows_by(\"PARAMCD\") %>% split_rows_by(\"GRADDR\") %>% count_abnormal_lab_worsen_by_baseline( var = \"ATOXGR\", variables = list( id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\" ) ) %>% append_topleft(\"Direction of Abnormality\") %>% build_table(df = df, alt_counts_df = tern_ex_adsl) #> ARM A ARM B ARM C #> Direction of Abnormality (N=69) (N=73) (N=58) #> ———————————————————————————————————————————————————————————————————————— #> IGA #> High #> 1 6/63 (9.5%) 6/64 (9.4%) 4/50 (8%) #> 2 8/64 (12.5%) 5/67 (7.5%) 8/53 (15.1%) #> 3 7/66 (10.6%) 5/68 (7.4%) 9/57 (15.8%) #> 4 6/68 (8.8%) 2/72 (2.8%) 3/58 (5.2%) #> Any 27/68 (39.7%) 18/72 (25%) 24/58 (41.4%) #> ALT #> High #> 1 7/63 (11.1%) 6/62 (9.7%) 2/48 (4.2%) #> 2 12/63 (19%) 4/67 (6%) 11/50 (22%) #> 3 4/65 (6.2%) 11/71 (15.5%) 7/56 (12.5%) #> 4 1/67 (1.5%) 8/71 (11.3%) 4/57 (7%) #> Any 24/67 (35.8%) 29/71 (40.8%) 24/57 (42.1%) #> Low #> 1 12/67 (17.9%) 4/66 (6.1%) 7/52 (13.5%) #> 2 9/68 (13.2%) 12/69 (17.4%) 6/55 (10.9%) #> 3 6/69 (8.7%) 4/71 (5.6%) 5/56 (8.9%) #> 4 7/69 (10.1%) 7/73 (9.6%) 6/58 (10.3%) #> Any 34/69 (49.3%) 27/73 (37%) 24/58 (41.4%) #> CRP #> Low #> 1 11/66 (16.7%) 10/67 (14.9%) 4/47 (8.5%) #> 2 8/66 (12.1%) 1/70 (1.4%) 6/50 (12%) #> 3 4/68 (5.9%) 9/70 (12.9%) 5/53 (9.4%) #> 4 7/69 (10.1%) 6/72 (8.3%) 4/55 (7.3%) #> Any 30/69 (43.5%) 26/72 (36.1%) 19/55 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Split function to configure risk difference column — add_riskdiff","title":"Split function to configure risk difference column — add_riskdiff","text":"Wrapper function rtables::add_combo_levels() configures settings risk difference column added rtables object. add risk difference column table, function used split_fun calls rtables::split_cols_by(), followed setting argument riskdiff TRUE following analyze function calls.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split function to configure risk difference column — add_riskdiff","text":"","code":"add_riskdiff( arm_x, arm_y, col_label = paste0(\"Risk Difference (%) (95% CI)\", if (length(arm_y) > 1) paste0(\"\\n\", arm_x, \" vs. \", arm_y)), pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split function to configure risk difference column — add_riskdiff","text":"arm_x (string) name reference arm use risk difference calculations. arm_y (character) names one arms compare reference arm risk difference calculations. new column added value arm_y. col_label (character) labels use rendering risk difference column within table. one comparison arm specified arm_y, default labels specify two arms compared (reference arm vs. comparison arm). pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split function to configure risk difference column — add_riskdiff","text":"closure suitable use split function (split_fun) within rtables::split_cols_by() creating table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split function to configure risk difference column — add_riskdiff","text":"","code":"adae <- tern_ex_adae adae$AESEV <- factor(adae$AESEV) lyt <- basic_table() %>% split_cols_by(\"ARMCD\", split_fun = add_riskdiff(arm_x = \"ARM A\", arm_y = c(\"ARM B\", \"ARM C\"))) %>% count_occurrences_by_grade( var = \"AESEV\", riskdiff = TRUE ) tbl <- build_table(lyt, df = adae) tbl #> Risk Difference (%) (95% CI) Risk Difference (%) (95% CI) #> ARM A ARM B ARM C ARM A vs. ARM B ARM A vs. ARM C #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> MILD 6 (3.0%) 4 (2.3%) 2 (1.2%) 0.7 (-2.5 - 3.9) 1.7 (-1.2 - 4.6) #> MODERATE 19 (9.4%) 15 (8.5%) 14 (8.6%) 0.9 (-4.8 - 6.7) 0.8 (-5.1 - 6.7) #> SEVERE 34 (16.8%) 38 (21.5%) 32 (19.8%) -4.6 (-12.6 - 3.3) -2.9 (-10.9 - 5.1)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout-creating function to add row total counts — add_rowcounts","title":"Layout-creating function to add row total counts — add_rowcounts","text":"works analogously rtables::add_colcounts() rows. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout-creating function to add row total counts — add_rowcounts","text":"","code":"add_rowcounts(lyt, alt_counts = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout-creating function to add row total counts — add_rowcounts","text":"lyt (PreDataTableLayouts) layout analyses added . alt_counts (flag) whether row counts taken alt_counts_df (TRUE) df (FALSE). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout-creating function to add row total counts — add_rowcounts","text":"modified layout latest row split labels now row-wise total counts (.e. without column-based subsetting) attached parentheses.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout-creating function to add row total counts — add_rowcounts","text":"Row count values contained row count rows displayed considered zero rows default pruning.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout-creating function to add row total counts — add_rowcounts","text":"","code":"basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% add_rowcounts() %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> —————————————————————————————————————————————————————————————————————————— #> ASIAN (N=231) #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 28.00 30.00 #> Median 33.00 32.50 33.50 #> Mean 34.20 32.68 34.63 #> 3rd Qu. 38.50 36.00 38.00 #> Max. 58.00 55.00 53.00 #> BLACK OR AFRICAN AMERICAN (N=79) #> Min. 23.00 21.00 24.00 #> 1st Qu. 29.00 28.75 29.00 #> Median 33.00 30.00 32.00 #> Mean 34.68 31.71 34.00 #> 3rd Qu. 37.25 36.25 39.00 #> Max. 60.00 42.00 51.00 #> WHITE (N=46) #> Min. 30.00 25.00 28.00 #> 1st Qu. 38.00 31.00 30.25 #> Median 40.50 37.50 35.00 #> Mean 39.36 36.93 35.11 #> 3rd Qu. 43.50 40.00 37.50 #> Max. 47.00 55.00 47.00"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for adverse event baskets — aesi_label","text":"","code":"aesi_label(aesi, scope = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for adverse event baskets — aesi_label","text":"aesi (character) vector standardized MedDRA query name (e.g. SMQxxNAM) customized query name (e.g. CQxxNAM). scope (character) vector scope query (e.g. SMQxxSC).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for adverse event baskets — aesi_label","text":"string standard label AE basket.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Labels for adverse event baskets — aesi_label","text":"","code":"adae <- tern_ex_adae # Standardized query label includes scope. aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC) #> [1] \"C.1.1.1.3/B.2.2.3.1 aesi (BROAD)\" # Customized query label. aesi_label(adae$CQ01NAM) #> [1] \"D.2.1.5.3/A.1.1.1.1 aesi\""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis function to calculate risk difference column values — afun_riskdiff","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"risk difference column, function uses statistics function associated afun calculates risk difference values arm X (reference group) arm Y. arms specified configuring risk difference column done using add_riskdiff() split function previous call rtables::split_cols_by(). columns, applies afun usual. function utilizes stat_propdiff_ci() function perform risk difference calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"","code":"afun_riskdiff( df, labelstr = \"\", .var, .N_col, .N_row, .df_row, .spl_context, .all_col_counts, .stats, .formats = NULL, .labels = NULL, .indent_mods = NULL, na_str = default_na_str(), afun, s_args = list() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .var (string) single variable name passed rtables requested statistics function. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. .spl_context (data.frame) gives information ancestor split states passed rtables. .all_col_counts (integer) vector value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_str (string) string used replace NA empty values output. afun (named list) named list containing one name-value pair name corresponds name statistics function used calculations value corresponding analysis function. s_args (named list) additional arguments passed statistics function analysis function supplied afun.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"list formatted rtables::CellValue().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get selected statistics names — afun_selected_stats","title":"Get selected statistics names — afun_selected_stats","text":"Helper function used creating afun.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get selected statistics names — afun_selected_stats","text":"","code":"afun_selected_stats(.stats, all_stats)"},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get selected statistics names — afun_selected_stats","text":".stats (vector NULL) input layout creating function. Note NULL means context default statistics used. all_stats (character) statistics can selected potentially.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get selected statistics names — afun_selected_stats","text":"character vector selected statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_colvars_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze functions in columns — analyze_colvars_functions","title":"Analyze functions in columns — analyze_colvars_functions","text":"functions wrappers rtables::analyze_colvars() apply corresponding tern statistics functions add analysis given table layout. particular, functions designed analysis methods split different columns. analyze_vars_in_cols(): fundamental tabulation analysis methods onto columns. words, analysis methods defined column space, .e. become column labels. changing variable vector, list functions can applied different variables, caveat number statistical functions. tabulate_rsp_subgroups(): similarly analyze_vars_in_cols, function combines analyze_colvars summarize_row_groups compact way produce standard tables show analysis methods columns. tabulate_survival_subgroups(): function similar , used tables. analyze_patients_exposure_in_cols(): based analyze_colvars. needs summarize_patients_exposure_in_cols() leverage nesting label rows analysis rtables::summarize_row_groups(). summarize_coxreg(): generally based rtables::summarize_row_groups(), behaves similarly tabulate_* functions described designed provide specific standard tables may contain nested structure combination summarize_row_groups() rtables::analyze_colvars().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze functions — analyze_functions","title":"Analyze functions — analyze_functions","text":"functions wrappers rtables::analyze() apply corresponding tern statistics functions add analysis given table layout: analyze_num_patients() analyze_vars() compare_vars() count_abnormal() count_abnormal_by_baseline() count_abnormal_by_marked() count_abnormal_by_worst_grade() count_cumulative() count_missed_doses() count_occurrences() count_occurrences_by_grade() count_patients_events_in_cols() count_patients_with_event() count_patients_with_flags() count_values() coxph_pairwise() estimate_incidence_rate() estimate_multinomial_rsp() estimate_odds_ratio() estimate_proportion() estimate_proportion_diff() summarize_ancova() summarize_colvars(): even function uses rtables::analyze_colvars(), applies analysis methods different rows one variables split different columns. comparison, analyze_colvars_functions leverage analyze_colvars context split rows analysis methods columns. summarize_change() surv_time() surv_timepoint() test_proportion_diff()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze variables — analyze_variables","title":"Analyze variables — analyze_variables","text":"analyze function analyze_vars() creates layout element summarize one variables, using S3 generic function s_summary() calculate list summary statistics. list available statistics numeric variables can viewed running get_stats(\"analyze_vars_numeric\") non-numeric variables running get_stats(\"analyze_vars_counts\"). Use .stats parameter specify statistics include output summary table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze variables — analyze_variables","text":"","code":"analyze_vars( lyt, vars, var_labels = vars, na_str = default_na_str(), nested = TRUE, ..., na.rm = TRUE, show_labels = \"default\", table_names = vars, section_div = NA_character_, .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...) # S3 method for class 'numeric' s_summary( x, na.rm = TRUE, denom, .N_row, .N_col, .var, control = control_analyze_vars(), ... ) # S3 method for class 'factor' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, ... ) # S3 method for class 'character' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, .var, verbose = TRUE, ... ) # S3 method for class 'logical' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, ... ) a_summary( x, .N_col, .N_row, .var = NULL, .df_row = NULL, .ref_group = NULL, .in_ref_col = FALSE, compare = FALSE, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, na.rm = TRUE, na_str = default_na_str(), ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze variables — analyze_variables","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_summary(). na.rm (flag) whether NA values removed x prior analysis. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric\") see statistics available numeric variables, get_stats(\"analyze_vars_counts\") statistics available non-numeric variables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. x (numeric) vector numbers want analyze. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .var (string) single variable name passed rtables requested statistics function. control (list) parameters descriptive statistics details, specified using helper function control_analyze_vars(). possible parameter options : conf_level (proportion) confidence level interval mean median. quantiles (numeric(2)) vector length two specify quantiles. quantile_type (numeric(1)) 1 9 selecting quantile algorithms used. See type stats::quantile(). test_mean (numeric(1)) value test mean null hypothesis calculating p-value. verbose (flag) defaults TRUE, prints warnings messages. mainly used print information factor casting. .df_row (data.frame) data frame across columns given row split. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. compare (flag) whether comparison statistics analyzed instead summary statistics (compare = TRUE adds pval statistic comparing reference group).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analyze variables — analyze_variables","text":"analyze_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_summary() table layout. s_summary() returns different statistics depending class x. x class numeric, returns list following named numeric items: n: length() x. sum: sum() x. mean: mean() x. sd: stats::sd() x. se: standard error x mean, .e.: (sd(x) / sqrt(length(x))). mean_sd: mean() stats::sd() x. mean_se: mean() x standard error (see ). mean_ci: CI mean x (stat_mean_ci()). mean_sei: SE interval mean x, .e.: (mean() -/+ stats::sd() / sqrt()). mean_sdi: SD interval mean x, .e.: (mean() -/+ stats::sd()). mean_pval: two-sided p-value mean x (stat_mean_pval()). median: stats::median() x. mad: median absolute deviation x, .e.: (stats::median() xc, xc = x - stats::median()). median_ci: CI median x (stat_median_ci()). quantiles: Two sample quantiles x (stats::quantile()). iqr: stats::IQR() x. range: range_noinf() x. min: max() x. max: min() x. median_range: median() range_noinf() x. cv: coefficient variation x, .e.: (stats::sd() / mean() * 100). geom_mean: geometric mean x, .e.: (exp(mean(log(x)))). geom_cv: geometric coefficient variation x, .e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100). x class factor converted character, returns list named numeric items: n: length() x. count: list number cases level factor x. count_fraction: Similar count also includes proportion cases level factor x relative denominator, NA denominator zero. x class logical, returns list named numeric items: n: length() x (possibly removing NAs). count: Count TRUE x. count_fraction: Count proportion TRUE x relative denominator, NA denominator zero. Note NAs x never counted leading NA . a_summary() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyze variables — analyze_variables","text":"Automatic digit formatting: number digits display can automatically determined analyzed variable(s) (vars) certain statistics setting statistic format \"auto\" .formats. utilizes format_auto() formatting function. Note data current row & variable (columns) considered (.df_row[[.var]], see rtables::additional_fun_params) whole dataset.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Analyze variables — analyze_variables","text":"analyze_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_summary(): S3 generic function produces variable summary. s_summary(numeric): Method numeric class. s_summary(factor): Method factor class. s_summary(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_summary(logical): Method logical class. a_summary(): Formatted analysis function used afun analyze_vars() compare_vars() cfun summarize_colvars().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Analyze variables — analyze_variables","text":"x empty vector, NA returned. expected feature return rcell content rtables intersection column row delimits empty data selection. mean function applied empty vector, NA returned instead NaN, latter standard behavior R. x empty factor, list still returned counts one element per factor level. levels x, function fails. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"\") also excluded na.rm set TRUE. Automatic conversion character factor guarantee table can generated correctly. particular sparse tables likely can fail. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table(). use comparison (additional p-value statistic), parameter compare must set TRUE. Ensure either NA values converted explicit NA level NA values left .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyze variables — analyze_variables","text":"","code":"## Fabricated dataset. dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)) ) # `analyze_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\") build_table(l, df = dta_test) #> A B C #> ———————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean (SD) 7.5 (2.1) 3.0 (NA) NA #> Median 7.5 3.0 NA #> Min - Max 6.0 - 9.0 3.0 - 3.0 NA #> V2 #> n 2 1 0 #> Mean (SD) 6.5 (2.1) 2.0 (NA) NA #> Median 6.5 2.0 NA #> Min - Max 5.0 - 8.0 2.0 - 2.0 NA #> V3 #> n 2 1 0 #> Mean (SD) 5.5 (2.1) 1.0 (NA) NA #> Median 5.5 1.0 NA #> Min - Max 4.0 - 7.0 1.0 - 1.0 NA ## Select and format statistics output. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars( vars = \"AVAL\", .stats = c(\"n\", \"mean_sd\", \"quantiles\"), .formats = c(\"mean_sd\" = \"xx.x, xx.x\"), .labels = c(n = \"n\", mean_sd = \"Mean, SD\", quantiles = c(\"Q1 - Q3\")) ) build_table(l, df = dta_test) #> A B C #> ——————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean, SD 7.5, 2.1 3.0, NA NA #> Q1 - Q3 6.0 - 9.0 3.0 - 3.0 NA #> V2 #> n 2 1 0 #> Mean, SD 6.5, 2.1 2.0, NA NA #> Q1 - Q3 5.0 - 8.0 2.0 - 2.0 NA #> V3 #> n 2 1 0 #> Mean, SD 5.5, 2.1 1.0, NA NA #> Q1 - Q3 4.0 - 7.0 1.0 - 1.0 NA ## Use arguments interpreted by `s_summary`. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\", na.rm = FALSE) build_table(l, df = dta_test) #> A B C #> ————————————————————————————————— #> V1 #> n 2 2 2 #> Mean (SD) 7.5 (2.1) NA NA #> Median 7.5 NA NA #> Min - Max 6.0 - 9.0 NA NA #> V2 #> n 2 2 2 #> Mean (SD) 6.5 (2.1) NA NA #> Median 6.5 NA NA #> Min - Max 5.0 - 8.0 NA NA #> V3 #> n 2 2 2 #> Mean (SD) 5.5 (2.1) NA NA #> Median 5.5 NA NA #> Min - Max 4.0 - 7.0 NA NA ## Handle `NA` levels first when summarizing factors. dta_test$AVISIT <- NA_character_ dta_test <- df_explicit_na(dta_test) l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze_vars(vars = \"AVISIT\", na.rm = FALSE) build_table(l, df = dta_test) #> A B C #> —————————————————————————————————————————— #> n 6 6 6 #> 6 (100%) 6 (100%) 6 (100%) # auto format dt <- data.frame(\"VAR\" = c(0.001, 0.2, 0.0011000, 3, 4)) basic_table() %>% analyze_vars( vars = \"VAR\", .stats = c(\"n\", \"mean\", \"mean_sd\", \"range\"), .formats = c(\"mean_sd\" = \"auto\", \"range\" = \"auto\") ) %>% build_table(dt) #> all obs #> ————————————————————————————— #> n 5 #> Mean 1.4 #> Mean (SD) 1.44042 (1.91481) #> Min - Max 0.0010 - 4.0000 # `s_summary.numeric` ## Basic usage: empty numeric returns NA-filled items. s_summary(numeric()) #> $n #> n #> 0 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NaN #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> ## Management of NA values. x <- c(NA_real_, 1) s_summary(x, na.rm = TRUE) #> $n #> n #> 1 #> #> $sum #> sum #> 1 #> #> $mean #> mean #> 1 #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> 1 NA #> #> $mean_se #> mean se #> 1 NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 1 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 1 1 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 0 #> #> $range #> min max #> 1 1 #> #> $min #> min #> 1 #> #> $max #> max #> 1 #> #> $median_range #> median min max #> 1 1 1 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> 1 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> s_summary(x, na.rm = FALSE) #> $n #> n #> 2 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NA #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> x <- c(NA_real_, 1, 2) s_summary(x, stats = NULL) #> $n #> n #> 2 #> #> $sum #> sum #> 3 #> #> $mean #> mean #> 1.5 #> #> $sd #> sd #> 0.7071068 #> #> $se #> se #> 0.5 #> #> $mean_sd #> mean sd #> 1.5000000 0.7071068 #> #> $mean_se #> mean se #> 1.5 0.5 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> -4.853102 7.853102 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 1 2 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 0.7928932 2.2071068 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 0.2048328 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 1.5 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 1 2 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 1 #> #> $range #> min max #> 1 2 #> #> $min #> min #> 1 #> #> $max #> max #> 2 #> #> $median_range #> median min max #> 1.5 1.0 2.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 47.14045 #> #> $geom_mean #> geom_mean #> 1.414214 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 0.01729978 115.60839614 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 52.10922 #> ## Benefits in `rtables` contructions: dta_test <- data.frame( Group = rep(LETTERS[1:3], each = 2), sub_group = rep(letters[1:2], each = 3), x = 1:6 ) ## The summary obtained in with `rtables`: basic_table() %>% split_cols_by(var = \"Group\") %>% split_rows_by(var = \"sub_group\") %>% analyze(vars = \"x\", afun = s_summary) %>% build_table(df = dta_test) #> A B C #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> a #> n 2 1 0 #> sum 3 3 NA #> mean 1.5 3 NA #> sd 0.707106781186548 NA NA #> se 0.5 NA NA #> mean_sd 1.5, 0.707106781186548 3, NA NA #> mean_se 1.5, 0.5 3, NA NA #> Mean 95% CI -4.85310236808735, 7.85310236808735 NA NA #> Mean -/+ 1xSE 1, 2 NA NA #> Mean -/+ 1xSD 0.792893218813452, 2.20710678118655 NA NA #> Mean p-value (H0: mean = 0) 0.204832764699133 NA NA #> median 1.5 3 NA #> mad 0 0 NA #> Median 95% CI NA NA NA #> 25% and 75%-ile 1, 2 3, 3 NA #> iqr 1 0 NA #> range 1, 2 3, 3 NA #> min 1 3 NA #> max 2 3 NA #> Median (Min - Max) 1.5, 1, 2 3, 3, 3 NA #> cv 47.1404520791032 NA NA #> geom_mean 1.41421356237309 3 NA #> Geometric Mean 95% CI 0.0172997815631007, 115.608396135236 NA NA #> geom_cv 52.1092246837487 NA NA #> b #> n 0 1 2 #> sum NA 4 11 #> mean NA 4 5.5 #> sd NA NA 0.707106781186548 #> se NA NA 0.5 #> mean_sd NA 4, NA 5.5, 0.707106781186548 #> mean_se NA 4, NA 5.5, 0.5 #> Mean 95% CI NA NA -0.853102368087347, 11.8531023680873 #> Mean -/+ 1xSE NA NA 5, 6 #> Mean -/+ 1xSD NA NA 4.79289321881345, 6.20710678118655 #> Mean p-value (H0: mean = 0) NA NA 0.0577158767526089 #> median NA 4 5.5 #> mad NA 0 0 #> Median 95% CI NA NA NA #> 25% and 75%-ile NA 4, 4 5, 6 #> iqr NA 0 1 #> range NA 4, 4 5, 6 #> min NA 4 5 #> max NA 4 6 #> Median (Min - Max) NA 4, 4, 4 5.5, 5, 6 #> cv NA NA 12.8564869306645 #> geom_mean NA 4 5.47722557505166 #> Geometric Mean 95% CI NA NA 1.71994304449266, 17.4424380482025 #> geom_cv NA NA 12.945835316564 ## By comparison with `lapply`: X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group))) lapply(X, function(x) s_summary(x$x)) #> $A.a #> $A.a$n #> n #> 2 #> #> $A.a$sum #> sum #> 3 #> #> $A.a$mean #> mean #> 1.5 #> #> $A.a$sd #> sd #> 0.7071068 #> #> $A.a$se #> se #> 0.5 #> #> $A.a$mean_sd #> mean sd #> 1.5000000 0.7071068 #> #> $A.a$mean_se #> mean se #> 1.5 0.5 #> #> $A.a$mean_ci #> mean_ci_lwr mean_ci_upr #> -4.853102 7.853102 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $A.a$mean_sei #> mean_sei_lwr mean_sei_upr #> 1 2 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $A.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 0.7928932 2.2071068 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $A.a$mean_pval #> p_value #> 0.2048328 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $A.a$median #> median #> 1.5 #> #> $A.a$mad #> mad #> 0 #> #> $A.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $A.a$quantiles #> quantile_0.25 quantile_0.75 #> 1 2 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $A.a$iqr #> iqr #> 1 #> #> $A.a$range #> min max #> 1 2 #> #> $A.a$min #> min #> 1 #> #> $A.a$max #> max #> 2 #> #> $A.a$median_range #> median min max #> 1.5 1.0 2.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $A.a$cv #> cv #> 47.14045 #> #> $A.a$geom_mean #> geom_mean #> 1.414214 #> #> $A.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 0.01729978 115.60839614 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $A.a$geom_cv #> geom_cv #> 52.10922 #> #> #> $B.a #> $B.a$n #> n #> 1 #> #> $B.a$sum #> sum #> 3 #> #> $B.a$mean #> mean #> 3 #> #> $B.a$sd #> sd #> NA #> #> $B.a$se #> se #> NA #> #> $B.a$mean_sd #> mean sd #> 3 NA #> #> $B.a$mean_se #> mean se #> 3 NA #> #> $B.a$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $B.a$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $B.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $B.a$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $B.a$median #> median #> 3 #> #> $B.a$mad #> mad #> 0 #> #> $B.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $B.a$quantiles #> quantile_0.25 quantile_0.75 #> 3 3 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $B.a$iqr #> iqr #> 0 #> #> $B.a$range #> min max #> 3 3 #> #> $B.a$min #> min #> 3 #> #> $B.a$max #> max #> 3 #> #> $B.a$median_range #> median min max #> 3 3 3 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $B.a$cv #> cv #> NA #> #> $B.a$geom_mean #> geom_mean #> 3 #> #> $B.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $B.a$geom_cv #> geom_cv #> NA #> #> #> $C.a #> $C.a$n #> n #> 0 #> #> $C.a$sum #> sum #> NA #> #> $C.a$mean #> mean #> NA #> #> $C.a$sd #> sd #> NA #> #> $C.a$se #> se #> NA #> #> $C.a$mean_sd #> mean sd #> NA NA #> #> $C.a$mean_se #> mean se #> NA NA #> #> $C.a$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $C.a$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $C.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $C.a$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $C.a$median #> median #> NA #> #> $C.a$mad #> mad #> NA #> #> $C.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $C.a$quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $C.a$iqr #> iqr #> NA #> #> $C.a$range #> min max #> NA NA #> #> $C.a$min #> min #> NA #> #> $C.a$max #> max #> NA #> #> $C.a$median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $C.a$cv #> cv #> NA #> #> $C.a$geom_mean #> geom_mean #> NaN #> #> $C.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $C.a$geom_cv #> geom_cv #> NA #> #> #> $A.b #> $A.b$n #> n #> 0 #> #> $A.b$sum #> sum #> NA #> #> $A.b$mean #> mean #> NA #> #> $A.b$sd #> sd #> NA #> #> $A.b$se #> se #> NA #> #> $A.b$mean_sd #> mean sd #> NA NA #> #> $A.b$mean_se #> mean se #> NA NA #> #> $A.b$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $A.b$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $A.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $A.b$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $A.b$median #> median #> NA #> #> $A.b$mad #> mad #> NA #> #> $A.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $A.b$quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $A.b$iqr #> iqr #> NA #> #> $A.b$range #> min max #> NA NA #> #> $A.b$min #> min #> NA #> #> $A.b$max #> max #> NA #> #> $A.b$median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $A.b$cv #> cv #> NA #> #> $A.b$geom_mean #> geom_mean #> NaN #> #> $A.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $A.b$geom_cv #> geom_cv #> NA #> #> #> $B.b #> $B.b$n #> n #> 1 #> #> $B.b$sum #> sum #> 4 #> #> $B.b$mean #> mean #> 4 #> #> $B.b$sd #> sd #> NA #> #> $B.b$se #> se #> NA #> #> $B.b$mean_sd #> mean sd #> 4 NA #> #> $B.b$mean_se #> mean se #> 4 NA #> #> $B.b$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $B.b$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $B.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $B.b$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $B.b$median #> median #> 4 #> #> $B.b$mad #> mad #> 0 #> #> $B.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $B.b$quantiles #> quantile_0.25 quantile_0.75 #> 4 4 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $B.b$iqr #> iqr #> 0 #> #> $B.b$range #> min max #> 4 4 #> #> $B.b$min #> min #> 4 #> #> $B.b$max #> max #> 4 #> #> $B.b$median_range #> median min max #> 4 4 4 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $B.b$cv #> cv #> NA #> #> $B.b$geom_mean #> geom_mean #> 4 #> #> $B.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $B.b$geom_cv #> geom_cv #> NA #> #> #> $C.b #> $C.b$n #> n #> 2 #> #> $C.b$sum #> sum #> 11 #> #> $C.b$mean #> mean #> 5.5 #> #> $C.b$sd #> sd #> 0.7071068 #> #> $C.b$se #> se #> 0.5 #> #> $C.b$mean_sd #> mean sd #> 5.5000000 0.7071068 #> #> $C.b$mean_se #> mean se #> 5.5 0.5 #> #> $C.b$mean_ci #> mean_ci_lwr mean_ci_upr #> -0.8531024 11.8531024 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $C.b$mean_sei #> mean_sei_lwr mean_sei_upr #> 5 6 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $C.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 4.792893 6.207107 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $C.b$mean_pval #> p_value #> 0.05771588 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $C.b$median #> median #> 5.5 #> #> $C.b$mad #> mad #> 0 #> #> $C.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $C.b$quantiles #> quantile_0.25 quantile_0.75 #> 5 6 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $C.b$iqr #> iqr #> 1 #> #> $C.b$range #> min max #> 5 6 #> #> $C.b$min #> min #> 5 #> #> $C.b$max #> max #> 6 #> #> $C.b$median_range #> median min max #> 5.5 5.0 6.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $C.b$cv #> cv #> 12.85649 #> #> $C.b$geom_mean #> geom_mean #> 5.477226 #> #> $C.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 1.719943 17.442438 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $C.b$geom_cv #> geom_cv #> 12.94584 #> #> # `s_summary.factor` ## Basic usage: s_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\"))) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> # Empty factor returns zero-filled items. s_summary(factor(levels = c(\"a\", \"b\", \"c\"))) #> $n #> [1] 0 #> #> $count #> $count$a #> [1] 0 #> #> $count$b #> [1] 0 #> #> $count$c #> [1] 0 #> #> #> $count_fraction #> $count_fraction$a #> [1] 0 0 #> #> $count_fraction$b #> [1] 0 0 #> #> $count_fraction$c #> [1] 0 0 #> #> #> $fraction #> $fraction$a #> num denom #> 0 0 #> #> $fraction$b #> num denom #> 0 0 #> #> $fraction$c #> num denom #> 0 0 #> #> #> $n_blq #> [1] 0 #> ## Management of NA values. x <- factor(c(NA, \"Female\")) x <- explicit_na(x) s_summary(x, na.rm = TRUE) #> $n #> [1] 1 #> #> $count #> $count$Female #> [1] 1 #> #> #> $count_fraction #> $count_fraction$Female #> [1] 1 1 #> #> #> $fraction #> $fraction$Female #> num denom #> 1 1 #> #> #> $n_blq #> [1] 0 #> s_summary(x, na.rm = FALSE) #> $n #> [1] 2 #> #> $count #> $count$Female #> [1] 1 #> #> $count$`` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$Female #> [1] 1.0 0.5 #> #> $count_fraction$`` #> [1] 1.0 0.5 #> #> #> $fraction #> $fraction$Female #> num denom #> 1 2 #> #> $fraction$`` #> num denom #> 1 2 #> #> #> $n_blq #> [1] 0 #> ## Different denominators. x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.3 #> #> $count_fraction$b #> [1] 1.0 0.1 #> #> $count_fraction$c #> [1] 1.0 0.1 #> #> #> $fraction #> $fraction$a #> num denom #> 3 10 #> #> $fraction$b #> num denom #> 1 10 #> #> $fraction$c #> num denom #> 1 10 #> #> #> $n_blq #> [1] 0 #> s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.00 0.15 #> #> $count_fraction$b #> [1] 1.00 0.05 #> #> $count_fraction$c #> [1] 1.00 0.05 #> #> #> $fraction #> $fraction$a #> num denom #> 3 20 #> #> $fraction$b #> num denom #> 1 20 #> #> $fraction$c #> num denom #> 1 20 #> #> #> $n_blq #> [1] 0 #> # `s_summary.character` ## Basic usage: s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\"), .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\", \"\"), .var = \"x\", na.rm = FALSE, verbose = FALSE) #> $n #> [1] 6 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`NA` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #> #> $count_fraction$b #> [1] 1.0000000 0.1666667 #> #> $count_fraction$c #> [1] 1.0000000 0.1666667 #> #> $count_fraction$`NA` #> [1] 1.0000000 0.1666667 #> #> #> $fraction #> $fraction$a #> num denom #> 3 6 #> #> $fraction$b #> num denom #> 1 6 #> #> $fraction$c #> num denom #> 1 6 #> #> $fraction$`NA` #> num denom #> 1 6 #> #> #> $n_blq #> [1] 0 #> # `s_summary.logical` ## Basic usage: s_summary(c(TRUE, FALSE, TRUE, TRUE)) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.75 #> #> $n_blq #> [1] 0 #> # Empty factor returns zero-filled items. s_summary(as.logical(c())) #> $n #> [1] 0 #> #> $count #> [1] 0 #> #> $count_fraction #> [1] 0 0 #> #> $n_blq #> [1] 0 #> ## Management of NA values. x <- c(NA, TRUE, FALSE) s_summary(x, na.rm = TRUE) #> $n #> [1] 2 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.5 #> #> $n_blq #> [1] 0 #> s_summary(x, na.rm = FALSE) #> $n #> [1] 3 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0000000 0.3333333 #> #> $n_blq #> [1] 0 #> ## Different denominators. x <- c(TRUE, FALSE, TRUE, TRUE) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.0 0.3 #> #> $n_blq #> [1] 0 #> s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.15 #> #> $n_blq #> [1] 0 #> a_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 5 0 n #> 2 a 3 0 a #> 3 b 1 0 b #> 4 c 1 0 c #> 5 a 3 (60%) 0 a #> 6 b 1 (20%) 0 b #> 7 c 1 (20%) 0 c #> 8 a 3 (60.0%) 0 a #> 9 b 1 (20.0%) 0 b #> 10 c 1 (20.0%) 0 c #> 11 a 3/5 (60.0%) 0 a #> 12 b 1/5 (20.0%) 0 b #> 13 c 1/5 (20.0%) 0 c #> 14 n_blq 0 0 n_blq a_summary( factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .ref_group = factor(c(\"a\", \"a\", \"b\", \"c\")), compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 5 0 #> 2 a 3 0 #> 3 b 1 0 #> 4 c 1 0 #> 5 a 3 (60%) 0 #> 6 b 1 (20%) 0 #> 7 c 1 (20%) 0 #> 8 a 3 (60.0%) 0 #> 9 b 1 (20.0%) 0 #> 10 c 1 (20.0%) 0 #> 11 a 3/5 (60.0%) 0 #> 12 b 1/5 (20.0%) 0 #> 13 c 1/5 (20.0%) 0 #> 14 n_blq 0 0 #> 15 p-value (chi-squared test) 0.9560 0 #> row_label #> 1 n #> 2 a #> 3 b #> 4 c #> 5 a #> 6 b #> 7 c #> 8 a #> 9 b #> 10 c #> 11 a #> 12 b #> 13 c #> 14 n_blq #> 15 p-value (chi-squared test) a_summary(c(\"A\", \"B\", \"A\", \"C\"), .var = \"x\", .N_col = 10, .N_row = 10, verbose = FALSE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 4 0 n #> 2 A 2 0 A #> 3 B 1 0 B #> 4 C 1 0 C #> 5 A 2 (50%) 0 A #> 6 B 1 (25%) 0 B #> 7 C 1 (25%) 0 C #> 8 A 2 (50.0%) 0 A #> 9 B 1 (25.0%) 0 B #> 10 C 1 (25.0%) 0 C #> 11 A 2/4 (50.0%) 0 A #> 12 B 1/4 (25.0%) 0 B #> 13 C 1/4 (25.0%) 0 C #> 14 n_blq 0 0 n_blq a_summary( c(\"A\", \"B\", \"A\", \"C\"), .ref_group = c(\"B\", \"A\", \"C\"), .var = \"x\", compare = TRUE, verbose = FALSE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 4 0 #> 2 A 2 0 #> 3 B 1 0 #> 4 C 1 0 #> 5 A 2 (50%) 0 #> 6 B 1 (25%) 0 #> 7 C 1 (25%) 0 #> 8 A 2 (50.0%) 0 #> 9 B 1 (25.0%) 0 #> 10 C 1 (25.0%) 0 #> 11 A 2/4 (50.0%) 0 #> 12 B 1/4 (25.0%) 0 #> 13 C 1/4 (25.0%) 0 #> 14 n_blq 0 0 #> 15 p-value (chi-squared test) 0.9074 0 #> row_label #> 1 n #> 2 A #> 3 B #> 4 C #> 5 A #> 6 B #> 7 C #> 8 A #> 9 B #> 10 C #> 11 A #> 12 B #> 13 C #> 14 n_blq #> 15 p-value (chi-squared test) a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 5 0 n #> 2 count 3 0 count #> 3 count_fraction 3 (60%) 0 count_fraction #> 4 count_fraction 3 (60.0%) 0 count_fraction #> 5 fraction 0 fraction #> 6 n_blq 0 0 n_blq a_summary( c(TRUE, FALSE, FALSE, TRUE, TRUE), .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 5 0 #> 2 count 3 0 #> 3 count_fraction 3 (60%) 0 #> 4 count_fraction 3 (60.0%) 0 #> 5 fraction 0 #> 6 n_blq 0 0 #> 7 p-value (chi-squared test) 0 #> row_label #> 1 n #> 2 count #> 3 count_fraction #> 4 count_fraction #> 5 fraction #> 6 n_blq #> 7 p-value (chi-squared test) a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = \"bla\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 10 0 #> 2 Sum -4.4 0 #> 3 Mean -0.4 0 #> 4 SD 1.1 0 #> 5 SE 0.4 0 #> 6 Mean (SD) -0.4 (1.1) 0 #> 7 Mean (SE) -0.4 (0.4) 0 #> 8 Mean 95% CI (-1.24, 0.36) 0 #> 9 Mean -/+ 1xSE (-0.79, -0.09) 0 #> 10 Mean -/+ 1xSD (-1.56, 0.68) 0 #> 11 Mean p-value (H0: mean = 0) 0.2432 0 #> 12 Median -0.2 0 #> 13 Median Absolute Deviation 0.0 0 #> 14 Median 95% CI (-1.82, 0.62) 0 #> 15 25% and 75%-ile -1.4 - 0.3 0 #> 16 IQR 1.7 0 #> 17 Min - Max -2.4 - 1.1 0 #> 18 Minimum -2.4 0 #> 19 Maximum 1.1 0 #> 20 Median (Min - Max) -0.2 (-2.4 - 1.1) 0 #> 21 CV (%) -253.2 0 #> 22 Geometric Mean NA 0 #> 23 Geometric Mean 95% CI NA 0 #> 24 CV % Geometric Mean NA 0 #> row_label #> 1 n #> 2 Sum #> 3 Mean #> 4 SD #> 5 SE #> 6 Mean (SD) #> 7 Mean (SE) #> 8 Mean 95% CI #> 9 Mean -/+ 1xSE #> 10 Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12 Median #> 13 Median Absolute Deviation #> 14 Median 95% CI #> 15 25% and 75%-ile #> 16 IQR #> 17 Min - Max #> 18 Minimum #> 19 Maximum #> 20 Median (Min - Max) #> 21 CV (%) #> 22 Geometric Mean #> 23 Geometric Mean 95% CI #> 24 CV % Geometric Mean a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = \"bla\", compare = TRUE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 10 0 #> 2 Sum 48.2 0 #> 3 Mean 4.8 0 #> 4 SD 1.2 0 #> 5 SE 0.4 0 #> 6 Mean (SD) 4.8 (1.2) 0 #> 7 Mean (SE) 4.8 (0.4) 0 #> 8 Mean 95% CI (3.98, 5.66) 0 #> 9 Mean -/+ 1xSE (4.45, 5.19) 0 #> 10 Mean -/+ 1xSD (3.65, 6.00) 0 #> 11 Mean p-value (H0: mean = 0) <0.0001 0 #> 12 Median 4.7 0 #> 13 Median Absolute Deviation 0.0 0 #> 14 Median 95% CI (3.37, 5.63) 0 #> 15 25% and 75%-ile 4.1 - 5.5 0 #> 16 IQR 1.5 0 #> 17 Min - Max 3.1 - 7.1 0 #> 18 Minimum 3.1 0 #> 19 Maximum 7.1 0 #> 20 Median (Min - Max) 4.7 (3.1 - 7.1) 0 #> 21 CV (%) 24.4 0 #> 22 Geometric Mean 4.7 0 #> 23 Geometric Mean 95% CI (3.93, 5.60) 0 #> 24 CV % Geometric Mean 25.2 0 #> 25 p-value (t-test) <0.0001 0 #> row_label #> 1 n #> 2 Sum #> 3 Mean #> 4 SD #> 5 SE #> 6 Mean (SD) #> 7 Mean (SE) #> 8 Mean 95% CI #> 9 Mean -/+ 1xSE #> 10 Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12 Median #> 13 Median Absolute Deviation #> 14 Median 95% CI #> 15 25% and 75%-ile #> 16 IQR #> 17 Min - Max #> 18 Minimum #> 19 Maximum #> 20 Median (Min - Max) #> 21 CV (%) #> 22 Geometric Mean #> 23 Geometric Mean 95% CI #> 24 CV % Geometric Mean #> 25 p-value (t-test)"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze numeric variables in columns — analyze_vars_in_cols","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"layout-creating function analyze_vars_in_cols() creates layout element generate column-wise analysis table. function sets analysis methods column labels wrapper rtables::analyze_colvars(). designed principally PK tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"","code":"analyze_vars_in_cols( lyt, vars, ..., .stats = c(\"n\", \"mean\", \"sd\", \"se\", \"cv\", \"geom_cv\"), .labels = c(n = \"n\", mean = \"Mean\", sd = \"SD\", se = \"SE\", cv = \"CV (%)\", geom_cv = \"CV % Geometric Mean\"), row_labels = NULL, do_summarize_row_groups = FALSE, split_col_vars = TRUE, imp_rule = NULL, avalcat_var = \"AVALCAT1\", cache = FALSE, .indent_mods = NULL, na_str = default_na_str(), nested = TRUE, .formats = NULL, .aligns = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). row_labels (character) function works columns space, usually .labels character vector applies column space. can change row labels defining parameter named character vector names corresponding split values. defaults NULL contains one string, duplicate row label. do_summarize_row_groups (flag) defaults FALSE applies analysis current label rows. wrapper rtables::summarize_row_groups() can accept labelstr define row labels. behavior supported never need overload row labels. split_col_vars (flag) defaults TRUE puts analysis results onto columns. option allows add multiple instances functions, also nested fashion, without adding splits. split must happen one time single layout. imp_rule (string NULL) imputation rule setting. Defaults NULL imputation rule. Can also \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. order use imputation rule, avalcat_var argument must specified. See imputation_rule() details imputation. avalcat_var (string) imp_rule NULL, name variable indicates whether row data corresponds analysis value category \"BLQ\", \"LTR\", \"% h_pkparam_sort() lyt <- basic_table() %>% split_rows_by(var = \"STRATA1\", label_pos = \"topleft\") %>% split_rows_by( var = \"SEX\", label_pos = \"topleft\", child_labels = \"hidden\" ) %>% # Removes duplicated labels analyze_vars_in_cols(vars = \"AGE\") result <- build_table(lyt = lyt, df = adpp) result #> STRATA1 #> SEX n Mean SD SE CV (%) CV % Geometric Mean #> ———————————————————————————————————————————————————————————————— #> A #> F 81 38.8 5.4 0.6 13.8 14.3 #> M 81 38.9 5.9 0.7 15.1 14.9 #> B #> F 90 36.0 6.4 0.7 17.7 17.9 #> M 81 36.4 6.5 0.7 17.9 17.8 #> C #> F 117 34.1 6.2 0.6 18.2 18.3 #> M 72 33.2 11.5 1.4 34.6 31.6 # By selecting just some statistics and ad-hoc labels lyt <- basic_table() %>% split_rows_by(var = \"ARM\", label_pos = \"topleft\") %>% split_rows_by( var = \"SEX\", label_pos = \"topleft\", child_labels = \"hidden\", split_fun = drop_split_levels ) %>% analyze_vars_in_cols( vars = \"AGE\", .stats = c(\"n\", \"cv\", \"geom_mean\"), .labels = c( n = \"aN\", cv = \"aCV\", geom_mean = \"aGeomMean\" ) ) result <- build_table(lyt = lyt, df = adpp) result #> ARM #> SEX aN aCV aGeomMean #> ——————————————————————————————————————— #> A: Drug X #> B: Placebo #> C: Combination #> F 288 17.6 35.5 #> M 234 23.4 35.3 # Changing row labels lyt <- basic_table() %>% analyze_vars_in_cols( vars = \"AGE\", row_labels = \"some custom label\" ) result <- build_table(lyt, df = adpp) result #> n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————————— #> some custom label 522 36.1 7.4 0.3 20.4 20.6 # Pharmacokinetic parameters lyt <- basic_table() %>% split_rows_by( var = \"TLG_DISPLAY\", split_label = \"PK Parameter\", label_pos = \"topleft\", child_labels = \"hidden\" ) %>% analyze_vars_in_cols( vars = \"AVAL\" ) result <- build_table(lyt, df = adpp) result #> PK Parameter n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax 58 29.7 5.6 0.7 19.0 19.3 #> AUCinf obs 58 207.5 34.9 4.6 16.8 17.3 #> CL obs 58 5.1 1.0 0.1 20.6 22.7 #> Ae 58 1.5 0.3 0.0 21.3 24.1 #> Fe 58 15.7 3.6 0.5 22.7 24.0 #> CLR 58 0.0 0.0 0.0 19.9 22.2 #> Rmax 58 9.6 2.0 0.3 21.1 21.6 #> Tonset 58 3.0 0.7 0.1 22.4 23.3 #> RENALCLD 58 0.0 0.0 0.0 19.0 19.4 # Multiple calls (summarize label and analyze underneath) lyt <- basic_table() %>% split_rows_by( var = \"TLG_DISPLAY\", split_label = \"PK Parameter\", label_pos = \"topleft\" ) %>% analyze_vars_in_cols( vars = \"AVAL\", do_summarize_row_groups = TRUE # does a summarize level ) %>% split_rows_by(\"SEX\", child_labels = \"hidden\", label_pos = \"topleft\" ) %>% analyze_vars_in_cols( vars = \"AVAL\", split_col_vars = FALSE # avoids re-splitting the columns ) result <- build_table(lyt, df = adpp) result #> PK Parameter #> SEX n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax 58 29.7 5.6 0.7 19.0 19.3 #> F 32 30.1 6.1 1.1 20.4 21.0 #> M 26 29.2 5.0 1.0 17.2 17.2 #> AUCinf obs 58 207.5 34.9 4.6 16.8 17.3 #> F 32 209.5 43.3 7.7 20.7 21.6 #> M 26 205.0 20.9 4.1 10.2 10.4 #> CL obs 58 5.1 1.0 0.1 20.6 22.7 #> F 32 5.1 1.1 0.2 20.8 23.2 #> M 26 5.1 1.1 0.2 20.7 22.5 #> Ae 58 1.5 0.3 0.0 21.3 24.1 #> F 32 1.5 0.3 0.1 19.3 20.8 #> M 26 1.5 0.4 0.1 23.9 27.9 #> Fe 58 15.7 3.6 0.5 22.7 24.0 #> F 32 16.3 3.4 0.6 20.9 22.3 #> M 26 15.0 3.7 0.7 24.7 25.6 #> CLR 58 0.0 0.0 0.0 19.9 22.2 #> F 32 0.0 0.0 0.0 20.5 24.8 #> M 26 0.0 0.0 0.0 19.5 18.9 #> Rmax 58 9.6 2.0 0.3 21.1 21.6 #> F 32 9.6 2.2 0.4 22.4 23.5 #> M 26 9.6 1.9 0.4 19.7 19.5 #> Tonset 58 3.0 0.7 0.1 22.4 23.3 #> F 32 3.0 0.7 0.1 24.7 25.9 #> M 26 3.0 0.6 0.1 19.8 20.0 #> RENALCLD 58 0.0 0.0 0.0 19.0 19.4 #> F 32 0.0 0.0 0.0 19.6 20.4 #> M 26 0.0 0.0 0.0 18.0 17.6"},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add variable labels to top left corner in table — append_varlabels","title":"Add variable labels to top left corner in table — append_varlabels","text":"Helper layout-creating function append variable labels given variables vector given dataset top left corner. variable label found variable name used instead. Multiple variable labels concatenated slashes.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add variable labels to top left corner in table — append_varlabels","text":"","code":"append_varlabels(lyt, df, vars, indent = 0L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add variable labels to top left corner in table — append_varlabels","text":"lyt (PreDataTableLayouts) layout analyses added . df (data.frame) data set containing analysis variables. vars (character) variable names labels looked df. indent (integer(1)) non-negative number nested indent space, default 0L means indent. 1L means two spaces indent, 2L means four spaces indent .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add variable labels to top left corner in table — append_varlabels","text":"modified layout new variable label(s) added top-left material.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add variable labels to top left corner in table — append_varlabels","text":"optimal implementation course, since using data set layout creation. mature rtables implementation also improved necessary anymore.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add variable labels to top left corner in table — append_varlabels","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"SEX\") %>% append_varlabels(DM, \"SEX\") %>% analyze(\"AGE\", afun = mean) %>% append_varlabels(DM, \"AGE\", indent = 1) build_table(lyt, DM) #> SEX A: Drug X B: Placebo C: Combination #> Age (N=121) (N=106) (N=129) #> ————————————————————————————————————————————————————————————————————————— #> F #> mean 33.7142857142857 33.8392857142857 34.8852459016393 #> M #> mean 36.5490196078431 32.1 34.2794117647059 #> U #> mean NA NA NA #> UNDIFFERENTIATED #> mean NA NA NA lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"AGE\", afun = mean) %>% append_varlabels(DM, c(\"SEX\", \"AGE\")) build_table(lyt, DM) #> SEX / Age A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————————— #> F #> mean 33.7142857142857 33.8392857142857 34.8852459016393 #> M #> mean 36.5490196078431 32.1 34.2794117647059 #> U #> mean NA NA NA #> UNDIFFERENTIATED #> mean NA NA NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply automatic formatting — apply_auto_formatting","title":"Apply automatic formatting — apply_auto_formatting","text":"Checks listed formats .formats \"auto\", replaces \"auto\" correct implementation format_auto given statistics, data, variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply automatic formatting — apply_auto_formatting","text":"","code":"apply_auto_formatting(.formats, x_stats, .df_row, .var)"},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply automatic formatting — apply_auto_formatting","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. x_stats (named list) named list statistics element corresponds element .formats, matching names. .df_row (data.frame) data frame across columns given row split. .var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":null,"dir":"Reference","previous_headings":"","what":"Standard arguments — argument_convention","title":"Standard arguments — argument_convention","text":"documentation function lists arguments tern used repeatedly express analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standard arguments — argument_convention","text":"... additional arguments lower level functions. .aligns (character NULL) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments. .all_col_counts (integer) vector value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .df_row (data.frame) data frame across columns given row split. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .ref_group (data.frame vector) data corresponding reference group. .spl_context (data.frame) gives information ancestor split states passed rtables. .stats (character) statistics select table. .var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. col_by (factor) defining column groups. conf_level (proportion) confidence level interval. data (data.frame) dataset containing variables summarize. df (data.frame) data set containing analysis variables. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. id (string) subject variable name. is_event (flag)TRUE event, FALSE time event censored. label_all (string) label total population analysis. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (PreDataTableLayouts) layout analyses added . method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed. na.rm (flag) whether NA values removed x prior analysis. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. prune_zero_rows (flag) whether prune zero rows. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. rsp (logical) vector indicating whether subject responder . show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. tte (numeric) vector time--event duration values. var_labels (character) variable labels. variables (named list string) list additional analysis variables. vars (character) variable names primary analysis variable iterated . var (string) single variable name primary analysis variable. x (numeric) vector numbers want analyze. xlim (numeric(2)) vector containing lower upper limits x-axis, respectively. NULL (default), default scale range used. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standard arguments — argument_convention","text":"Although function just returns NULL two uses, tern users provides documentation arguments commonly consistently used framework. developer adds single reference point import roxygen argument description : @inheritParams argument_convention","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange multiple grobs — arrange_grobs","title":"Arrange multiple grobs — arrange_grobs","text":"Arrange grobs new grob n * m (rows * cols) layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange multiple grobs — arrange_grobs","text":"","code":"arrange_grobs( ..., grobs = list(...), ncol = NULL, nrow = NULL, padding_ht = grid::unit(2, \"line\"), padding_wt = grid::unit(2, \"line\"), vp = NULL, gp = NULL, name = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange multiple grobs — arrange_grobs","text":"... grobs. grobs (list grob) list grobs. ncol (integer(1)) number columns layout. nrow (integer(1)) number rows layout. padding_ht (grid::unit) unit length 1, vertical space grob. padding_wt (grid::unit) unit length 1, horizontal space grob. vp (viewport NULL) viewport() object (NULL). gp (gpar) gpar() object. name (string) character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange multiple grobs — arrange_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange multiple grobs — arrange_grobs","text":"","code":"library(grid) # \\donttest{ num <- lapply(1:9, textGrob) grid::grid.newpage() grid.draw(arrange_grobs(grobs = num, ncol = 2)) #> Warning: `arrange_grobs()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. showViewport() g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, nrow = 2)) showViewport() grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, ncol = 3)) grid::grid.newpage() grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2))) vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1)) showViewport() # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to rtable — as.rtable","title":"Convert to rtable — as.rtable","text":"new generic function convert objects rtable tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to rtable — as.rtable","text":"","code":"as.rtable(x, ...) # S3 method for class 'data.frame' as.rtable(x, format = \"xx.xx\", ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to rtable — as.rtable","text":"x (data.frame) object converted rtable. ... additional arguments methods. format (string function) format used columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to rtable — as.rtable","text":"rtables table object. Note concrete class depend method used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Convert to rtable — as.rtable","text":".rtable(data.frame): Method converting data.frame contains numeric columns rtable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to rtable — as.rtable","text":"","code":"x <- data.frame( a = 1:10, b = rnorm(10) ) as.rtable(x) #> a b #> —————————————————— #> 1 1.00 0.07 #> 2 2.00 -0.64 #> 3 3.00 -0.05 #> 4 4.00 -0.25 #> 5 5.00 0.44 #> 6 6.00 2.76 #> 7 7.00 0.05 #> 8 8.00 0.58 #> 9 9.00 0.12 #> 10 10.00 -1.91"},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of a vector to a factor — as_factor_keep_attributes","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"Converts x factor keeps attributes. Warns appropriately user can decide whether prefer converting factor manually (e.g. full control factor levels).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"","code":"as_factor_keep_attributes( x, x_name = deparse(substitute(x)), na_level = \"\", verbose = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"x (vector) object convert. x_name (string) name x. na_level (string) explicit missing level used converting character vector. verbose (flag) defaults TRUE. prints warnings messages.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"factor attributes (except class) x. modify x already factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional assertions to use with checkmate — assertions","title":"Additional assertions to use with checkmate — assertions","text":"Additional assertion functions can used together checkmate package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional assertions to use with checkmate — assertions","text":"","code":"assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL) assert_df_with_variables( df, variables, na_level = NULL, .var.name = checkmate::vname(df), add = NULL ) assert_valid_factor( x, min.levels = 1, max.levels = NULL, null.ok = TRUE, any.missing = TRUE, n.levels = NULL, len = NULL, .var.name = checkmate::vname(x), add = NULL ) assert_df_with_factors( df, variables, min.levels = 1, max.levels = NULL, any.missing = TRUE, na_level = NULL, .var.name = checkmate::vname(df), add = NULL ) assert_proportion_value(x, include_boundaries = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional assertions to use with checkmate — assertions","text":"x () object test. .var.name [character(1)] Name checked object print assertions. Defaults heuristic implemented vname. add [AssertCollection] Collection store assertion messages. See AssertCollection. df (data.frame) data set test. variables (named list character) list variables test. na_level (string) string using represent NA missing data. NA values please consider using directly .na() similar approaches. min.levels [integer(1)] Minimum number factor levels. Default NULL (check). max.levels [integer(1)] Maximum number factor levels. Default NULL (check). null.ok [logical(1)] set TRUE, x may also NULL. case type check x performed, additional checks disabled. .missing [logical(1)] vectors missing values allowed? Default TRUE. n.levels [integer(1)] Exact number factor levels. Default NULL (check). len [integer(1)] Exact expected length x. include_boundaries (flag) whether include boundaries testing proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional assertions to use with checkmate — assertions","text":"Nothing assertion passes, otherwise prints error message.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Additional assertions to use with checkmate — assertions","text":"assert_list_of_variables(): Checks whether x valid list variable names. NULL elements list x dropped Filter(Negate(.null), x). assert_df_with_variables(): Check whether df data frame analysis variables. Please notice produces error variables present data.frame opposite required. assert_valid_factor(): Check whether x valid factor (.e. levels empty string levels). Note NULL NA elements allowed. assert_df_with_factors(): Check whether df data frame analysis variables factors. Note creation NA direct call factor() trim NA levels vector list . assert_proportion_value(): Check whether x proportion: number 0 1.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for bins in percent — bins_percent_labels","title":"Labels for bins in percent — bins_percent_labels","text":"creates labels quantile based bins percent. assumes right-closed intervals produced cut_quantile_bins().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for bins in percent — bins_percent_labels","text":"","code":"bins_percent_labels(probs, digits = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for bins in percent — bins_percent_labels","text":"probs (numeric) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. digits (integer(1)) number decimal places round percent numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for bins in percent — bins_percent_labels","text":"character vector labels format [0%,20%], (20%,50%], etc.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":null,"dir":"Reference","previous_headings":"","what":"Bland-Altman analysis — bland_altman","title":"Bland-Altman analysis — bland_altman","text":"Functions use Bland-Altman method assess agreement two numerical vectors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bland-Altman analysis — bland_altman","text":"","code":"s_bland_altman(x, y, conf_level = 0.95) g_bland_altman(x, y, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bland-Altman analysis — bland_altman","text":"x (numeric) vector numbers want analyze. y (numeric) vector numbers want analyze, compared x. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bland-Altman analysis — bland_altman","text":"s_bland_altman() returns named list following elements: df, difference_mean, ci_mean, difference_sd, difference_se, upper_agreement_limit, lower_agreement_limit, agreement_limit_se, upper_agreement_limit_ci, lower_agreement_limit_ci, t_value, n. g_bland_altman() returns ggplot Bland-Altman plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Bland-Altman analysis — bland_altman","text":"s_bland_altman(): Statistics function compares two numeric vectors using Bland-Altman method calculates variety statistics. g_bland_altman(): Graphing function produces Bland-Altman plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bland-Altman analysis — bland_altman","text":"","code":"x <- seq(1, 60, 5) y <- seq(5, 50, 4) conf_level <- 0.9 # Derive statistics that are needed for Bland-Altman plot s_bland_altman(x, y, conf_level = conf_level) #> $df #> average difference #> 1 3.0 -4 #> 2 7.5 -3 #> 3 12.0 -2 #> 4 16.5 -1 #> 5 21.0 0 #> 6 25.5 1 #> 7 30.0 2 #> 8 34.5 3 #> 9 39.0 4 #> 10 43.5 5 #> 11 48.0 6 #> 12 52.5 7 #> #> $difference_mean #> [1] 1.5 #> #> $ci_mean #> [1] -0.3692162 3.3692162 #> #> $difference_sd #> [1] 3.605551 #> #> $difference_se #> [1] 1.040833 #> #> $upper_agreement_limit #> [1] 7.430604 #> #> $lower_agreement_limit #> [1] -4.430604 #> #> $agreement_limit_se #> [1] 1.802776 #> #> $upper_agreement_limit_ci #> [1] 4.193027 10.668181 #> #> $lower_agreement_limit_ci #> [1] -7.668181 -1.193027 #> #> $t_value #> [1] 1.795885 #> #> $n #> [1] 12 #> # Create a Bland-Altman plot g_bland_altman(x = x, y = y, conf_level = conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Content row function to add row total to labels — c_label_n","title":"Content row function to add row total to labels — c_label_n","text":"takes label latest row split level adds row total df parentheses. function differs c_label_n_alt() taking row counts df rather alt_counts_df, used add_rowcounts() alt_counts set FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content row function to add row total to labels — c_label_n","text":"","code":"c_label_n(df, labelstr, .N_row)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content row function to add row total to labels — c_label_n","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content row function to add row total to labels — c_label_n","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Content row function to add row total to labels — c_label_n","text":"important use df rather .N_row implementation, former already split columns refer first column data .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":null,"dir":"Reference","previous_headings":"","what":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"takes label latest row split level adds row total alt_counts_df parentheses. function differs c_label_n() taking row counts alt_counts_df rather df, used add_rowcounts() alt_counts set TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"","code":"c_label_n_alt(df, labelstr, .alt_df_row)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for content functions given a data frame with flag input — cfun_by_flag","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"can useful tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"","code":"cfun_by_flag(analysis_var, flag_var, format = \"xx\", .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"analysis_var (string) variable name column containing values returned content function. flag_var (string) variable name logical column identifying row returned. format (string)rtables format use.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"content function gives df$analysis_var row identified .df_row$flag given format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Check proportion difference arguments — check_diff_prop_ci","title":"Check proportion difference arguments — check_diff_prop_ci","text":"Verifies /convert arguments valid values used estimation difference responder proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check proportion difference arguments — check_diff_prop_ci","text":"","code":"check_diff_prop_ci(rsp, grp, strata = NULL, conf_level, correct = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check proportion difference arguments — check_diff_prop_ci","text":"rsp (logical) vector indicating whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). strata (factor) variable one level per stratum length rsp. conf_level (proportion) confidence level interval. correct (flag) whether include continuity correction. information, see stats::prop.test().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Check element dimension — check_same_n","title":"Check element dimension — check_same_n","text":"Checks elements ... dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check element dimension — check_same_n","text":"","code":"check_same_n(..., omit_null = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check element dimension — check_same_n","text":"... (data.frame vector) data frames vectors. omit_null (flag) whether NULL elements ... omitted check.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check element dimension — check_same_n","text":"logical value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Class for CombinationFunction — combination_function","title":"Class for CombinationFunction — combination_function","text":"CombinationFunction S4 class extends standard functions. special functions can combined negated logical operators.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class for CombinationFunction — combination_function","text":"","code":"# S4 method for class 'CombinationFunction,CombinationFunction' e1 & e2 # S4 method for class 'CombinationFunction,CombinationFunction' e1 | e2 # S4 method for class 'CombinationFunction' !x"},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class for CombinationFunction — combination_function","text":"e1 (CombinationFunction) left hand side logical operator. e2 (CombinationFunction) right hand side logical operator. x (CombinationFunction) function negated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Class for CombinationFunction — combination_function","text":"logical value indicating whether left hand side equation equals right hand side.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Class for CombinationFunction — combination_function","text":"e1 & e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. e1 | e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. `!`(CombinationFunction): Logical negation CombinationFunction functions. resulting object class, evaluates original function. result opposite results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Class for CombinationFunction — combination_function","text":"","code":"higher <- function(a) { force(a) CombinationFunction( function(x) { x > a } ) } lower <- function(b) { force(b) CombinationFunction( function(x) { x < b } ) } c1 <- higher(5) c2 <- lower(10) c3 <- higher(5) & lower(10) c3(7) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine counts — combine_counts","title":"Combine counts — combine_counts","text":"Simplifies estimation column counts, especially group combination required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine counts — combine_counts","text":"","code":"combine_counts(fct, groups_list = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine counts — combine_counts","text":"fct (factor) variable levels needs grouped. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine counts — combine_counts","text":"vector column counts.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine counts — combine_counts","text":"","code":"ref <- c(\"A: Drug X\", \"B: Placebo\") groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts( fct = DM$ARM, groups_list = groups ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM, col_counts = col_counts) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ————————————————————————————————————————————————— #> n 227 129 #> Mean (SD) 34.0 (7.2) 34.6 (6.5) #> Median 33.0 33.0 #> Min - Max 20.0 - 60.0 22.0 - 53.0 ref <- \"A: Drug X\" groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts( fct = DM$ARM, groups_list = groups ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM, col_counts = col_counts) #> A: Drug X B: Placebo/C: Combination #> (N=121) (N=235) #> ——————————————————————————————————————————————————— #> n 121 235 #> Mean (SD) 34.9 (7.8) 33.9 (6.5) #> Median 33.0 33.0 #> Min - Max 20.0 - 60.0 21.0 - 55.0"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference and treatment group combination — combine_groups","title":"Reference and treatment group combination — combine_groups","text":"Facilitate re-combination groups divided reference treatment groups; helps arranging groups columns rtables framework teal modules.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reference and treatment group combination — combine_groups","text":"","code":"combine_groups(fct, ref = NULL, collapse = \"/\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reference and treatment group combination — combine_groups","text":"fct (factor) variable levels needs grouped. ref (character) reference level(s). collapse (string) character string separate fct ref.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reference and treatment group combination — combine_groups","text":"list first item ref (reference) second item trt (treatment).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reference and treatment group combination — combine_groups","text":"","code":"groups <- combine_groups( fct = DM$ARM, ref = c(\"B: Placebo\") ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM) #> B: Placebo A: Drug X/C: Combination #> (N=106) (N=250) #> —————————————————————————————————————————————————— #> n 106 250 #> Mean (SD) 33.0 (6.3) 34.7 (7.1) #> Median 32.0 33.0 #> Min - Max 21.0 - 55.0 20.0 - 60.0"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine factor levels — combine_levels","title":"Combine factor levels — combine_levels","text":"Combine specified old factor Levels single new level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine factor levels — combine_levels","text":"","code":"combine_levels(x, levels, new_level = paste(levels, collapse = \"/\"))"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine factor levels — combine_levels","text":"x (factor) factor variable. levels (character) level names combined. new_level (string) name new level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine factor levels — combine_levels","text":"factor new levels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine factor levels — combine_levels","text":"","code":"x <- factor(letters[1:5], levels = letters[5:1]) combine_levels(x, levels = c(\"a\", \"b\")) #> [1] a/b a/b c d e #> Levels: e d c a/b combine_levels(x, c(\"e\", \"b\")) #> [1] a e/b c d e/b #> Levels: e/b d c a"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise combination of two vectors — combine_vectors","title":"Element-wise combination of two vectors — combine_vectors","text":"Element-wise combination two vectors","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise combination of two vectors — combine_vectors","text":"","code":"combine_vectors(x, y)"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise combination of two vectors — combine_vectors","text":"x (vector) first vector combine. y (vector) second vector combine.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise combination of two vectors — combine_vectors","text":"list element combines corresponding elements x y.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise combination of two vectors — combine_vectors","text":"","code":"combine_vectors(1:3, 4:6) #> [[1]] #> [1] 1 4 #> #> [[2]] #> [1] 2 5 #> #> [[3]] #> [1] 3 6 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare variables between groups — compare_variables","title":"Compare variables between groups — compare_variables","text":"analyze function compare_vars() creates layout element summarize compare one variables, using S3 generic function s_summary() calculate list summary statistics. list available statistics numeric variables can viewed running get_stats(\"analyze_vars_numeric\", add_pval = TRUE) non-numeric variables running get_stats(\"analyze_vars_counts\", add_pval = TRUE). Use .stats parameter specify statistics include output summary table. Prior using function table layout must use rtables::split_cols_by() create column split variable used comparisons, specify reference group via ref_group parameter. Comparisons can performed group (column) specified reference group including p-value statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare variables between groups — compare_variables","text":"","code":"compare_vars( lyt, vars, var_labels = vars, na_str = default_na_str(), nested = TRUE, ..., na.rm = TRUE, show_labels = \"default\", table_names = vars, section_div = NA_character_, .stats = c(\"n\", \"mean_sd\", \"count_fraction\", \"pval\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_compare(x, .ref_group, .in_ref_col, ...) # S3 method for class 'numeric' s_compare(x, .ref_group, .in_ref_col, ...) # S3 method for class 'factor' s_compare(x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, ...) # S3 method for class 'character' s_compare( x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, .var, verbose = TRUE, ... ) # S3 method for class 'logical' s_compare(x, .ref_group, .in_ref_col, na.rm = TRUE, denom = \"n\", ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare variables between groups — compare_variables","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_compare(). na.rm (flag) whether NA values removed x prior analysis. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric\", add_pval = TRUE) see statistics available numeric variables, get_stats(\"analyze_vars_counts\", add_pval = TRUE) statistics available non-numeric variables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. x (numeric) vector numbers want analyze. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. denom (string) choice denominator factor proportions, can n (number values row column intersection). .var (string) single variable name passed rtables requested statistics function. verbose (flag) whether warnings messages printed. Mainly used print information factor casting. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare variables between groups — compare_variables","text":"compare_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_compare() table layout. s_compare() returns output s_summary() comparisons versus reference group form p-values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compare variables between groups — compare_variables","text":"compare_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_compare(): S3 generic function produce comparison summary. s_compare(numeric): Method numeric class. uses standard t-test calculate p-value. s_compare(factor): Method factor class. uses chi-squared test calculate p-value. s_compare(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_compare(logical): Method logical class. chi-squared test used. missing values removed, counted FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare variables between groups — compare_variables","text":"factor variables, denom factor proportions can n since purpose compare proportions columns, therefore row-based proportion make sense. Proportion based N_col difficult since use counts chi-squared test statistic, therefore missing values accounted explicit factor levels. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"\") also excluded na.rm set TRUE. character variables, automatic conversion factor guarantee table generated correctly. particular sparse tables likely can fail. Therefore always better manually convert character variables factors pre-processing. compare_vars(), column split must define reference group via ref_group comparison well defined.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare variables between groups — compare_variables","text":"","code":"# `compare_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. lyt <- basic_table() %>% split_cols_by(\"ARMCD\", ref_group = \"ARM B\") %>% compare_vars(c(\"AGE\", \"SEX\")) build_table(lyt, tern_ex_adsl) #> ARM A ARM B ARM C #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 69 73 58 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) #> p-value (t-test) 0.1446 0.8212 #> SEX #> n 69 73 58 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) #> p-value (chi-squared test) 1.0000 1.0000 ## Select and format statistics output. lyt <- basic_table() %>% split_cols_by(\"ARMCD\", ref_group = \"ARM C\") %>% compare_vars( vars = \"AGE\", .stats = c(\"mean_sd\", \"pval\"), .formats = c(mean_sd = \"xx.x, xx.x\"), .labels = c(mean_sd = \"Mean, SD\") ) build_table(lyt, df = tern_ex_adsl) #> ARM A ARM B ARM C #> ———————————————————————————————————————————————————— #> Mean, SD 34.1, 6.8 35.8, 7.1 36.1, 7.4 #> p-value (t-test) 0.1176 0.8212 # `s_compare.numeric` ## Usual case where both this and the reference group vector have more than 1 value. s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE) #> $n #> n #> 10 #> #> $sum #> sum #> 51.27191 #> #> $mean #> mean #> 5.127191 #> #> $sd #> sd #> 1.226119 #> #> $se #> se #> 0.387733 #> #> $mean_sd #> mean sd #> 5.127191 1.226119 #> #> $mean_se #> mean se #> 5.127191 0.387733 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> 4.250078 6.004304 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 4.739458 5.514924 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 3.901071 6.353310 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 3.353908e-07 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 5.024369 #> #> $mad #> mad #> -4.440892e-16 #> #> $median_ci #> median_ci_lwr median_ci_upr #> 4.638779 5.862086 #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 4.756763 5.549828 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 0.7930643 #> #> $range #> min max #> 2.725885 7.682557 #> #> $min #> min #> 2.725885 #> #> $max #> max #> 7.682557 #> #> $median_range #> median min max #> 5.024369 2.725885 7.682557 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 23.91406 #> #> $geom_mean #> geom_mean #> 4.985435 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 4.144463 5.997052 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 26.26258 #> #> $pval #> [1] 2.25779e-08 #> ## If one group has not more than 1 value, then p-value is not calculated. s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE) #> $n #> n #> 10 #> #> $sum #> sum #> 50.71578 #> #> $mean #> mean #> 5.071578 #> #> $sd #> sd #> 1.105832 #> #> $se #> se #> 0.3496948 #> #> $mean_sd #> mean sd #> 5.071578 1.105832 #> #> $mean_se #> mean se #> 5.0715780 0.3496948 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> 4.280513 5.862643 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 4.721883 5.421273 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 3.965746 6.177410 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 1.511204e-07 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 5.260423 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> 3.529264 6.318293 #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 4.024149 6.065057 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 2.040908 #> #> $range #> min max #> 3.300549 6.337320 #> #> $min #> min #> 3.300549 #> #> $max #> max #> 6.33732 #> #> $median_range #> median min max #> 5.260423 3.300549 6.337320 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 21.8045 #> #> $geom_mean #> geom_mean #> 4.952266 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 4.181833 5.864639 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 23.97201 #> #> $pval #> character(0) #> ## Empty numeric does not fail, it returns NA-filled items and no p-value. s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE) #> $n #> n #> 0 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NaN #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> #> $pval #> character(0) #> # `s_compare.factor` ## Basic usage: x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) y <- factor(c(\"a\", \"b\", \"c\")) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> ## Management of NA values. x <- explicit_na(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\", NA, NA))) y <- explicit_na(factor(c(\"a\", \"b\", \"c\", NA))) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 7 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`` #> [1] 2 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0000000 0.4285714 #> #> $count_fraction$b #> [1] 1.0000000 0.1428571 #> #> $count_fraction$c #> [1] 1.0000000 0.1428571 #> #> $count_fraction$`` #> [1] 2.0000000 0.2857143 #> #> #> $fraction #> $fraction$a #> num denom #> 3 7 #> #> $fraction$b #> num denom #> 1 7 #> #> $fraction$c #> num denom #> 1 7 #> #> $fraction$`` #> num denom #> 2 7 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.9063036 #> # `s_compare.character` ## Basic usage: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\") y <- c(\"a\", \"b\", \"c\") s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> ## Note that missing values handling can make a large difference: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\", NA) y <- c(\"a\", \"b\", \"c\", rep(NA, 20)) s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE ) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", na.rm = FALSE, verbose = FALSE ) #> $n #> [1] 6 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #> #> $count_fraction$b #> [1] 1.0000000 0.1666667 #> #> $count_fraction$c #> [1] 1.0000000 0.1666667 #> #> $count_fraction$`` #> [1] 1.0000000 0.1666667 #> #> #> $fraction #> $fraction$a #> num denom #> 3 6 #> #> $fraction$b #> num denom #> 1 6 #> #> $fraction$c #> num denom #> 1 6 #> #> $fraction$`` #> num denom #> 1 6 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.005768471 #> # `s_compare.logical` ## Basic usage: x <- c(TRUE, FALSE, TRUE, TRUE) y <- c(FALSE, FALSE, TRUE) s_compare(x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.75 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.2702894 #> ## Management of NA values. x <- c(NA, TRUE, FALSE) y <- c(NA, NA, NA, NA, FALSE) s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 2 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.5 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.3864762 #> s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 3 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0000000 0.3333333 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.1675463 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for descriptive statistics — control_analyze_vars","title":"Control function for descriptive statistics — control_analyze_vars","text":"Sets list parameters summaries descriptive statistics. Typically used internally specify details s_summary(). function family mainly used analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for descriptive statistics — control_analyze_vars","text":"","code":"control_analyze_vars( conf_level = 0.95, quantiles = c(0.25, 0.75), quantile_type = 2, test_mean = 0 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for descriptive statistics — control_analyze_vars","text":"conf_level (proportion) confidence level interval. quantiles (numeric(2)) vector length two specify quantiles calculate. quantile_type (numeric(1)) number 1 9 selecting quantile algorithms used. Default set 2 matches default quantile algorithm SAS proc univariate set QNTLDEF=5. differs R's default. See type stats::quantile(). test_mean (numeric(1)) number test mean null hypothesis calculating p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for descriptive statistics — control_analyze_vars","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Control functions for Kaplan-Meier plot annotation tables — control_annot","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"Auxiliary functions controlling arguments formatting annotation tables can added plots generated via g_km().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"","code":"control_surv_med_annot(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE) control_coxph_annot( x = 0.29, y = 0.51, w = 0.4, h = 0.125, fill = TRUE, ref_lbls = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"x (proportion) x-coordinate center annotation table. y (proportion) y-coordinate center annotation table. w (proportion) relative width annotation table. h (proportion) relative height annotation table. fill (flag character) whether annotation table background fill color. Can also color code use background fill color. TRUE, color code defaults \"#00000020\". ref_lbls (flag) whether reference group explicitly printed labels annotation table. FALSE (default), comparison groups printed table labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"control_surv_med_annot(): Control function formatting median survival time annotation table. annotation table can added g_km() setting annot_surv_med=TRUE, can configured using control_surv_med_annot() function setting control_annot_surv_med argument. control_coxph_annot(): Control function formatting Cox-PH annotation table. annotation table can added g_km() setting annot_coxph=TRUE, can configured using control_coxph_annot() function setting control_annot_coxph argument.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"","code":"control_surv_med_annot() #> $x #> [1] 0.8 #> #> $y #> [1] 0.85 #> #> $w #> [1] 0.32 #> #> $h #> [1] 0.16 #> #> $fill #> [1] TRUE #> control_coxph_annot() #> $x #> [1] 0.29 #> #> $y #> [1] 0.51 #> #> $w #> [1] 0.4 #> #> $h #> [1] 0.125 #> #> $fill #> [1] TRUE #> #> $ref_lbls #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for Cox-PH model — control_coxph","title":"Control function for Cox-PH model — control_coxph","text":"auxiliary function controlling arguments Cox-PH model, typically used internally specify details Cox-PH model s_coxph_pairwise(). conf_level refers Hazard Ratio estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for Cox-PH model — control_coxph","text":"","code":"control_coxph( pval_method = c(\"log-rank\", \"wald\", \"likelihood\"), ties = c(\"efron\", \"breslow\", \"exact\"), conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for Cox-PH model — control_coxph","text":"pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) string specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for Cox-PH model — control_coxph","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for Cox regression — control_coxreg","title":"Control function for Cox regression — control_coxreg","text":"Sets list parameters Cox regression fit. Used internally.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for Cox regression — control_coxreg","text":"","code":"control_coxreg( pval_method = c(\"wald\", \"likelihood\"), ties = c(\"exact\", \"efron\", \"breslow\"), conf_level = 0.95, interaction = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for Cox regression — control_coxreg","text":"pval_method (string) method used estimation p.values; wald (default) likelihood. ties (string) among exact (equivalent DISCRETE SAS), efron breslow, see survival::coxph(). Note: equivalent SAS EXACT method R. conf_level (proportion) confidence level interval. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for Cox regression — control_coxreg","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for Cox regression — control_coxreg","text":"","code":"control_coxreg() #> $pval_method #> [1] \"wald\" #> #> $ties #> [1] \"exact\" #> #> $conf_level #> [1] 0.95 #> #> $interaction #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for incidence rate — control_incidence_rate","title":"Control function for incidence rate — control_incidence_rate","text":"auxiliary function controlling arguments incidence rate, used internally specify details s_incidence_rate().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate( conf_level = 0.95, conf_type = c(\"normal\", \"normal_log\", \"exact\", \"byar\"), input_time_unit = c(\"year\", \"day\", \"week\", \"month\"), num_pt_year = 100 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for incidence rate — control_incidence_rate","text":"conf_level (proportion) confidence level interval. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric(1)) number patient-years use calculating adverse event rates.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for incidence rate — control_incidence_rate","text":"list components names arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(0.9, \"exact\", \"month\", 100) #> $conf_level #> [1] 0.9 #> #> $conf_type #> [1] \"exact\" #> #> $input_time_unit #> [1] \"month\" #> #> $num_pt_year #> [1] 100 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for g_lineplot() — control_lineplot_vars","title":"Control function for g_lineplot() — control_lineplot_vars","text":"Default values variables parameter g_lineplot function. variable's default value can overwritten variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for g_lineplot() — control_lineplot_vars","text":"","code":"control_lineplot_vars( x = \"AVISIT\", y = \"AVAL\", group_var = \"ARM\", facet_var = NA, paramcd = \"PARAMCD\", y_unit = \"AVALU\", subject_var = \"USUBJID\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for g_lineplot() — control_lineplot_vars","text":"x (string) x-variable name. y (string) y-variable name. group_var (string NA) group variable name. facet_var (string NA) faceting variable name. paramcd (string NA) parameter code variable name. y_unit (string NA) y-axis unit variable name. subject_var (string NA) subject variable name.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for g_lineplot() — control_lineplot_vars","text":"named character vector variable names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for g_lineplot() — control_lineplot_vars","text":"","code":"control_lineplot_vars() #> x y group_var paramcd y_unit subject_var #> \"AVISIT\" \"AVAL\" \"ARM\" \"PARAMCD\" \"AVALU\" \"USUBJID\" #> facet_var #> NA control_lineplot_vars(group_var = NA) #> x y group_var paramcd y_unit subject_var #> \"AVISIT\" \"AVAL\" NA \"PARAMCD\" \"AVALU\" \"USUBJID\" #> facet_var #> NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for logistic regression model fitting — control_logistic","title":"Control function for logistic regression model fitting — control_logistic","text":"auxiliary function controlling arguments logistic regression models. conf_level refers confidence level used Odds Ratio CIs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for logistic regression model fitting — control_logistic","text":"","code":"control_logistic(response_definition = \"response\", conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for logistic regression model fitting — control_logistic","text":"response_definition (string) definition event terms response. used fitting logistic regression model left hand side formula. Note evaluated expression result either logical vector factor 2 levels. default just \"response\" original response variable used modified . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for logistic regression model fitting — control_logistic","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for logistic regression model fitting — control_logistic","text":"","code":"# Standard options. control_logistic() #> $response_definition #> [1] \"response\" #> #> $conf_level #> [1] 0.95 #> # Modify confidence level. control_logistic(conf_level = 0.9) #> $response_definition #> [1] \"response\" #> #> $conf_level #> [1] 0.9 #> # Use a different response definition. control_logistic(response_definition = \"I(response %in% c('CR', 'PR'))\") #> $response_definition #> [1] \"I(response %in% c('CR', 'PR'))\" #> #> $conf_level #> [1] 0.95 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for risk difference column — control_riskdiff","title":"Control function for risk difference column — control_riskdiff","text":"Sets list parameters use generating risk (proportion) difference column. Used input riskdiff parameter tabulate_rsp_subgroups() tabulate_survival_subgroups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for risk difference column — control_riskdiff","text":"","code":"control_riskdiff( arm_x = NULL, arm_y = NULL, format = \"xx.x (xx.x - xx.x)\", col_label = \"Risk Difference (%) (95% CI)\", pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for risk difference column — control_riskdiff","text":"arm_x (string) name reference arm use risk difference calculations. arm_y (character) names one arms compare reference arm risk difference calculations. new column added value arm_y. format (string function) format label (string) formatting function apply risk difference statistic. See 3d string options list_valid_format_labels() possible format strings. Defaults \"xx.x (xx.x - xx.x)\". col_label (character) labels use rendering risk difference column within table. one comparison arm specified arm_y, default labels specify two arms compared (reference arm vs. comparison arm). pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for risk difference column — control_riskdiff","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for risk difference column — control_riskdiff","text":"","code":"control_riskdiff() #> $arm_x #> NULL #> #> $arm_y #> NULL #> #> $format #> [1] \"xx.x (xx.x - xx.x)\" #> #> $col_label #> [1] \"Risk Difference (%) (95% CI)\" #> #> $pct #> [1] TRUE #> control_riskdiff(arm_x = \"ARM A\", arm_y = \"ARM B\") #> $arm_x #> [1] \"ARM A\" #> #> $arm_y #> [1] \"ARM B\" #> #> $format #> [1] \"xx.x (xx.x - xx.x)\" #> #> $col_label #> [1] \"Risk Difference (%) (95% CI)\" #> #> $pct #> [1] TRUE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"auxiliary function controlling arguments STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"","code":"control_step( biomarker = NULL, use_percentile = TRUE, bandwidth, degree = 0L, num_points = 39L )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"biomarker (numeric NULL) optional provision numeric biomarker variable, used infer bandwidth, see . use_percentile (flag) TRUE, running windows created according quantiles rather actual values, .e. bandwidth refers percentage data covered window. Suggest TRUE biomarker variable uniformly distributed. bandwidth (numeric(1) NULL) indicating bandwidth window. Depending argument use_percentile, can either length actual-value windows real biomarker scale, percentage windows. use_percentile = TRUE, number 0 1. NULL, treat bandwidth infinity, means one global model fitted. default, 0.25 used percentage windows one quarter range biomarker variable actual-value windows. degree (integer(1)) degree polynomial function biomarker interaction term treatment arm fitted window. 0 (default), biomarker variable included model fitted biomarker window. num_points (integer(1)) number points hazard ratios estimated. smallest number 2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"list components names arguments, except biomarker just used calculate bandwidth case actual biomarker windows requested.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"","code":"# Provide biomarker values and request actual values to be used, # so that bandwidth is chosen from range. control_step(biomarker = 1:10, use_percentile = FALSE) #> $use_percentile #> [1] FALSE #> #> $bandwidth #> [1] 2.25 #> #> $degree #> [1] 0 #> #> $num_points #> [1] 39 #> # Use a global model with quadratic biomarker interaction term. control_step(bandwidth = NULL, degree = 2) #> $use_percentile #> [1] TRUE #> #> $bandwidth #> NULL #> #> $degree #> [1] 2 #> #> $num_points #> [1] 39 #> # Reduce number of points to be used. control_step(num_points = 10) #> $use_percentile #> [1] TRUE #> #> $bandwidth #> [1] 0.25 #> #> $degree #> [1] 0 #> #> $num_points #> [1] 10 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for survfit models for survival time — control_surv_time","title":"Control function for survfit models for survival time — control_surv_time","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_time(). conf_level refers survival time estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for survfit models for survival time — control_surv_time","text":"","code":"control_surv_time( conf_level = 0.95, conf_type = c(\"plain\", \"log\", \"log-log\"), quantiles = c(0.25, 0.75) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for survfit models for survival time — control_surv_time","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. quantiles (numeric(2)) vector length two specifying quantiles survival time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for survfit models for survival time — control_surv_time","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_timepoint(). conf_level refers patient risk estimation time point.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"","code":"control_surv_timepoint( conf_level = 0.95, conf_type = c(\"plain\", \"log\", \"log-log\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative counts of numeric variable by thresholds — count_cumulative","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"analyze function count_cumulative() creates layout element calculate cumulative counts values numeric variable less , less equal , greater , greater equal user-specified threshold values. function analyzes numeric variable vars threshold values supplied thresholds argument numeric vector. Whether counts include threshold values, whether count values lower higher threshold values can set via include_eq lower_tail parameters, respectively.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"","code":"count_cumulative( lyt, vars, thresholds, lower_tail = TRUE, include_eq = TRUE, var_labels = vars, show_labels = \"visible\", na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_cumulative( x, thresholds, lower_tail = TRUE, include_eq = TRUE, .N_col, ... ) a_count_cumulative( x, thresholds, lower_tail = TRUE, include_eq = TRUE, .N_col, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . thresholds (numeric) vector cutoff values counts. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_cumulative\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"count_cumulative() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_cumulative() table layout. s_count_cumulative() returns named list count_fractions: list thresholds value component, component containing vector count fraction. a_count_cumulative() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"count_cumulative(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_cumulative(): Statistics function produces named list given numeric vector thresholds. a_count_cumulative(): Formatted analysis function used afun count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"","code":"basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_cumulative( vars = \"AGE\", thresholds = c(40, 60) ) %>% build_table(tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> —————————————————————————————————————————————————— #> AGE #> <= 40 52 (75.4%) 58 (79.5%) 41 (70.7%) #> <= 60 69 (100%) 73 (100%) 58 (100%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":null,"dir":"Reference","previous_headings":"","what":"Count number of patients with missed doses by thresholds — count_missed_doses","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"analyze function creates layout element calculate cumulative counts patients number missed doses least equal user-specified threshold values. function analyzes numeric variable vars, variable numbers missed doses, threshold values supplied thresholds argument numeric vector. function assumes every row given data frame corresponds unique patient.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"","code":"count_missed_doses( lyt, vars, thresholds, var_labels = vars, show_labels = \"visible\", na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_nonmissing(x) s_count_missed_doses(x, thresholds, .N_col) a_count_missed_doses(x, thresholds, .N_col)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . thresholds (numeric) minimum number missed doses patients . var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_missed_doses\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"count_missed_doses() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_missed_doses() table layout. s_count_nonmissing() returns statistic n count non-missing values x. s_count_missed_doses() returns statistics n count_fraction one element threshold. a_count_missed_doses() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"count_missed_doses(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_nonmissing(): Statistics function count non-missing values. s_count_missed_doses(): Statistics function count patients missed doses. a_count_missed_doses(): Formatted analysis function used afun count_missed_doses().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"","code":"library(dplyr) anl <- tern_ex_adsl %>% distinct(STUDYID, USUBJID, ARM) %>% mutate( PARAMCD = \"TNDOSMIS\", PARAM = \"Total number of missed doses during study\", AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE), AVALC = \"\" ) basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_missed_doses(\"AVAL\", thresholds = c(1, 5, 10, 15), var_labels = \"Missed Doses\") %>% build_table(anl, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————— #> Missed Doses #> n 69 73 58 #> At least 1 missed dose 69 (100%) 69 (94.5%) 55 (94.8%) #> At least 5 missed doses 57 (82.6%) 55 (75.3%) 50 (86.2%) #> At least 10 missed doses 40 (58%) 40 (54.8%) 32 (55.2%) #> At least 15 missed doses 26 (37.7%) 25 (34.2%) 13 (22.4%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences — count_occurrences","title":"Count occurrences — count_occurrences","text":"analyze function count_occurrences() creates layout element calculate occurrence counts patients. function analyzes variable(s) supplied vars returns table occurrence counts unique value (level) variable(s). variable (variables) must non-numeric. id variable used indicate unique subject identifiers (defaults USUBJID). multiple occurrences value recorded patient, value counted . summarize function summarize_occurrences() performs function count_occurrences() except creates content rows, data rows, summarize current table row/column context operates level latest row split root table row splits occurred.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences — count_occurrences","text":"","code":"count_occurrences( lyt, vars, id = \"USUBJID\", drop = TRUE, var_labels = vars, show_labels = \"hidden\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction_fixed_dp\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) summarize_occurrences( lyt, var, id = \"USUBJID\", drop = TRUE, riskdiff = FALSE, na_str = default_na_str(), ..., .stats = \"count_fraction_fixed_dp\", .formats = NULL, .indent_mods = NULL, .labels = NULL ) s_count_occurrences( df, denom = c(\"N_col\", \"n\"), .N_col, .df_row, drop = TRUE, .var = \"MHDECOD\", id = \"USUBJID\" ) a_count_occurrences( df, labelstr = \"\", id = \"USUBJID\", denom = c(\"N_col\", \"n\"), drop = TRUE, .N_col, .var = NULL, .df_row = NULL, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, na_str = default_na_str() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences — count_occurrences","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . id (string) subject variable name. drop (flag) whether non-appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_occurrences\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. denom (string) choice denominator patient proportions. Can : N_col: total number patients column across rows n: number patients occurrences .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .df_row (data.frame) data frame across columns given row split. .var, var (string) single variable name passed rtables requested statistics function. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences — count_occurrences","text":"count_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences() table layout. summarize_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences() table layout. s_count_occurrences() returns list : count: list counts one element per occurrence. count_fraction: list counts fractions one element per occurrence. fraction: list numerators denominators one element per occurrence. a_count_occurrences() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count occurrences — count_occurrences","text":"count_occurrences(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_occurrences(): Statistics function counts number patients report occurrence. a_count_occurrences(): Formatted analysis function used afun count_occurrences().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count occurrences — count_occurrences","text":"default, occurrences appear given row split dropped table occurrences table sorted alphabetically per row split. Therefore, corresponding layout needs use split_fun = drop_split_levels split_rows_by calls. Use drop = FALSE like show occurrences.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences — count_occurrences","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c( 1, 1, 2, 4, 4, 4, 6, 6, 6, 7, 7, 8 )), MHDECOD = c( \"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\", \"MH2\", \"MH2\", \"MH3\", \"MH1\", \"MH2\", \"MH4\" ), ARM = rep(c(\"A\", \"B\"), each = 6), SEX = c(\"F\", \"F\", \"M\", \"M\", \"M\", \"M\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\") ) df_adsl <- df %>% select(USUBJID, ARM) %>% unique() # Create table layout lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences(vars = \"MHDECOD\", .stats = c(\"count_fraction\")) # Apply table layout to data and produce `rtable` object tbl <- lyt %>% build_table(df, alt_counts_df = df_adsl) %>% prune_table() tbl #> A B #> (N=3) (N=3) #> ——————————————————————————— #> MH1 3 (100%) 1 (33.3%) #> MH2 1 (33.3%) 2 (66.7%) #> MH3 1 (33.3%) 1 (33.3%) #> MH4 0 1 (33.3%) # Layout creating function with custom format. basic_table() %>% add_colcounts() %>% split_rows_by(\"SEX\", child_labels = \"visible\") %>% summarize_occurrences( var = \"MHDECOD\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> ————————————————— #> F #> MH1 1 (16.7%) #> MH2 2 (33.3%) #> MH3 1 (16.7%) #> MH4 1 (16.7%) #> M #> MH1 3 (50.0%) #> MH2 1 (16.7%) #> MH3 1 (16.7%) # Count unique occurrences per subject. s_count_occurrences( df, .N_col = 4L, .df_row = df, .var = \"MHDECOD\", id = \"USUBJID\" ) #> $count #> $count$MH1 #> [1] 4 #> #> $count$MH2 #> [1] 3 #> #> $count$MH3 #> [1] 2 #> #> $count$MH4 #> [1] 1 #> #> #> $count_fraction #> $count_fraction$MH1 #> [1] 4 1 #> #> $count_fraction$MH2 #> [1] 3.00 0.75 #> #> $count_fraction$MH3 #> [1] 2.0 0.5 #> #> $count_fraction$MH4 #> [1] 1.00 0.25 #> #> #> $fraction #> $fraction$MH1 #> num denom #> 4 4 #> #> $fraction$MH2 #> num denom #> 3 4 #> #> $fraction$MH3 #> num denom #> 2 4 #> #> $fraction$MH4 #> num denom #> 1 4 #> #> a_count_occurrences( df, .N_col = 4L, .df_row = df, .var = \"MHDECOD\", id = \"USUBJID\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 MH1 4 0 MH1 #> 2 MH2 3 0 MH2 #> 3 MH3 2 0 MH3 #> 4 MH4 1 0 MH4 #> 5 MH1 4 (100%) 0 MH1 #> 6 MH2 3 (75%) 0 MH2 #> 7 MH3 2 (50%) 0 MH3 #> 8 MH4 1 (25%) 0 MH4 #> 9 MH1 4 (100%) 0 MH1 #> 10 MH2 3 (75.0%) 0 MH2 #> 11 MH3 2 (50.0%) 0 MH3 #> 12 MH4 1 (25.0%) 0 MH4 #> 13 MH1 4/4 (100.0%) 0 MH1 #> 14 MH2 3/4 (75.0%) 0 MH2 #> 15 MH3 2/4 (50.0%) 0 MH3 #> 16 MH4 1/4 (25.0%) 0 MH4"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences by grade — count_occurrences_by_grade","title":"Count occurrences by grade — count_occurrences_by_grade","text":"analyze function count_occurrences_by_grade() creates layout element calculate occurrence counts grade. function analyzes primary analysis variable var indicates toxicity grades. id variable used indicate unique subject identifiers (defaults USUBJID). user can also supply list custom groups grades analyze via grade_groups parameter. remove_single argument remove single grades analysis grade groups analyzed. multiple grades recorded one patient highest grade level counted. summarize function summarize_occurrences_by_grade() performs function count_occurrences_by_grade() except creates content rows, data rows, summarize current table row/column context operates level latest row split root table row splits occurred.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences by grade — count_occurrences_by_grade","text":"","code":"count_occurrences_by_grade( lyt, var, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, var_labels = var, show_labels = \"default\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = var, .stats = NULL, .formats = NULL, .indent_mods = NULL, .labels = NULL ) summarize_occurrences_by_grade( lyt, var, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, na_str = default_na_str(), ..., .stats = NULL, .formats = NULL, .indent_mods = NULL, .labels = NULL ) s_count_occurrences_by_grade( df, .var, .N_col, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, labelstr = \"\" ) a_count_occurrences_by_grade( df, .var, .N_col, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, labelstr = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences by grade — count_occurrences_by_grade","text":"lyt (PreDataTableLayouts) layout analyses added . id (string) subject variable name. grade_groups (named list character) list containing groupings grades. remove_single (flag)TRUE include elements one-element grade groups output list; case grade groups names included output. only_grade_groups set TRUE argument ignored. only_grade_groups (flag) whether specified grade groups included, individual grade rows removed (TRUE), grades grade groups displayed (FALSE). var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_occurrences_by_grade\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences by grade — count_occurrences_by_grade","text":"count_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences_by_grade() table layout. summarize_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences_by_grade() table layout. s_count_occurrences_by_grade() returns list counts fractions one element per grade level grade level grouping. a_count_occurrences_by_grade() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count occurrences by grade — count_occurrences_by_grade","text":"count_occurrences_by_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function counts number patients highest grade. a_count_occurrences_by_grade(): Formatted analysis function used afun count_occurrences_by_grade().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences by grade — count_occurrences_by_grade","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c(1:6, 1)), ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"A\"), levels = c(\"A\", \"B\")), AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)), AESEV = factor( x = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"MILD\", \"MILD\", \"MODERATE\", \"SEVERE\"), levels = c(\"MILD\", \"MODERATE\", \"SEVERE\") ), stringsAsFactors = FALSE ) df_adsl <- df %>% select(USUBJID, ARM) %>% unique() # Layout creating function with custom format. basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences_by_grade( var = \"AESEV\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> A B #> (N=3) (N=3) #> ———————————————————————————————————————— #> MILD 0.00 (0.00%) 2.00 (66.67%) #> MODERATE 1.00 (33.33%) 1.00 (33.33%) #> SEVERE 2.00 (66.67%) 0.00 (0.00%) # Define additional grade groupings. grade_groups <- list( \"-Any-\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-5\" = c(\"3\", \"4\", \"5\") ) basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences_by_grade( var = \"AETOXGR\", grade_groups = grade_groups, only_grade_groups = TRUE ) %>% build_table(df, alt_counts_df = df_adsl) #> A B #> (N=3) (N=3) #> ————————————————————————————————— #> -Any- 3 (100%) 3 (100%) #> Grade 1-2 1 (33.3%) 2 (66.7%) #> Grade 3-5 2 (66.7%) 1 (33.3%) # Layout creating function with custom format. basic_table() %>% add_colcounts() %>% split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>% summarize_occurrences_by_grade( var = \"AESEV\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> —————————————————————————— #> A #> MILD 0.00 (0.00%) #> MODERATE 1.00 (16.67%) #> SEVERE 2.00 (33.33%) #> B #> MILD 2.00 (33.33%) #> MODERATE 1.00 (16.67%) #> SEVERE 0.00 (0.00%) basic_table() %>% add_colcounts() %>% split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>% summarize_occurrences_by_grade( var = \"AETOXGR\", grade_groups = grade_groups ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> ——————————————————————— #> A #> -Any- 3 (50.0%) #> Grade 1-2 1 (16.7%) #> 1 0 #> 2 1 (16.7%) #> Grade 3-5 2 (33.3%) #> 3 2 (33.3%) #> 4 0 #> 5 0 #> B #> -Any- 3 (50.0%) #> Grade 1-2 2 (33.3%) #> 1 1 (16.7%) #> 2 1 (16.7%) #> Grade 3-5 1 (16.7%) #> 3 0 #> 4 1 (16.7%) #> 5 0 s_count_occurrences_by_grade( df, .N_col = 10L, .var = \"AETOXGR\", id = \"USUBJID\", grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> $count_fraction #> $count_fraction$ANY #> [1] 6.0 0.6 #> #> $count_fraction$`1` #> [1] 1.0 0.1 #> #> $count_fraction$`2` #> [1] 2.0 0.2 #> #> $count_fraction$`3` #> [1] 2.0 0.2 #> #> $count_fraction$`4` #> [1] 1.0 0.1 #> #> $count_fraction$`5` #> [1] 0 0 #> #> # We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences_by_grade, .ungroup_stats = \"count_fraction\") afun( df, .N_col = 10L, .var = \"AETOXGR\", id = \"USUBJID\", grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 ANY 6 (60.0%) 0 ANY #> 2 1 1 (10.0%) 0 1 #> 3 2 2 (20.0%) 0 2 #> 4 3 2 (20.0%) 0 3 #> 5 4 1 (10.0%) 0 4 #> 6 5 0 0 5"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patient events in columns — count_patients_events_in_cols","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize function summarize_patients_events_in_cols() creates layout element summarize patient event counts columns. function analyzes elements (events) supplied via filters_list parameter returns row counts number patients event well total numbers patients events. id variable used indicate unique subject identifiers (defaults USUBJID). multiple occurrences event recorded patient, event counted .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patient events in columns — count_patients_events_in_cols","text":"","code":"summarize_patients_events_in_cols( lyt, id = \"USUBJID\", filters_list = list(), empty_stats = character(), na_str = default_na_str(), ..., .stats = c(\"unique\", \"all\", names(filters_list)), .labels = c(unique = \"Patients (All)\", all = \"Events (All)\", labels_or_names(filters_list)), col_split = TRUE ) s_count_patients_and_multiple_events( df, id, filters_list, empty_stats = character(), labelstr = \"\", custom_label = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patient events in columns — count_patients_events_in_cols","text":"lyt (PreDataTableLayouts) layout analyses added . id (string) subject variable name. filters_list (named list character) list element list describes one type event describe filters, format s_count_patients_with_event(). label, used column title. empty_stats (character) optional names statistics returned empty corresponding table cells stay blank. na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"summarize_patients_events_in_cols\") see available statistics function, addition added using filters_list. .labels (named character) labels statistics (without indent). col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe. df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. custom_label (string NULL) provided labelstr empty used label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize_patients_events_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_patients_and_multiple_events() table layout. s_count_patients_and_multiple_events() returns list statistics: unique: number unique patients df. : number rows df. one element name filters_list: number rows df, .e. events, fulfilling filter condition.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize_patients_events_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function counts numbers patients multiple events defined filters. Used analysis function afun summarize_patients_events_in_cols().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patient events in columns — count_patients_events_in_cols","text":"","code":"df <- data.frame( USUBJID = rep(c(\"id1\", \"id2\", \"id3\", \"id4\"), c(2, 3, 1, 1)), ARM = c(\"A\", \"A\", \"B\", \"B\", \"B\", \"B\", \"A\"), AESER = rep(\"Y\", 7), AESDTH = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"N\"), AEREL = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"Y\"), AEDECOD = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"C\", \"D\"), AEBODSYS = rep(c(\"SOC1\", \"SOC2\", \"SOC3\"), c(3, 3, 1)) ) # `summarize_patients_events_in_cols()` basic_table() %>% summarize_patients_events_in_cols( filters_list = list( related = formatters::with_label(c(AEREL = \"Y\"), \"Events (Related)\"), fatal = c(AESDTH = \"Y\"), fatal_related = c(AEREL = \"Y\", AESDTH = \"Y\") ), custom_label = \"%s Total number of patients and events\" ) %>% build_table(df) #> Patients (All) Events (All) Events (Related) fatal fatal_related #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> %s Total number of patients and events 4 7 5 4 4"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of patients with a particular event — count_patients_with_event","title":"Count the number of patients with a particular event — count_patients_with_event","text":"analyze function count_patients_with_event() creates layout element calculate patient counts user-specified set events. function analyzes primary analysis variable vars indicates unique subject identifiers. Events defined user named vector via filters argument, name corresponds variable value value(s) variable takes event. multiple records event recorded patient, one occurrence counted.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of patients with a particular event — count_patients_with_event","text":"","code":"count_patients_with_event( lyt, vars, filters, riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_patients_with_event( df, .var, filters, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) a_count_patients_with_event( df, .var, filters, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of patients with a particular event — count_patients_with_event","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . filters (character) character vector specifying column names flag variables used counting number unique identifiers satisfying conditions. Multiple column names flags accepted format c(\"column_name1\" = \"flag1\", \"column_name2\" = \"flag2\"). Note equality accepted condition. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_patients_with_event\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) name column contains unique identifier. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of patients with a particular event — count_patients_with_event","text":"count_patients_with_event() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_event() table layout. s_count_patients_with_event() returns count fraction unique identifiers defined event. a_count_patients_with_event() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the number of patients with a particular event — count_patients_with_event","text":"count_patients_with_event(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_patients_with_event(): Statistics function counts number patients defined event occurred. a_count_patients_with_event(): Formatted analysis function used afun count_patients_with_event().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of patients with a particular event — count_patients_with_event","text":"","code":"# `count_patients_with_event()` lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_values( \"STUDYID\", values = \"AB12345\", .stats = \"count\", .labels = c(count = \"Total AEs\") ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\"), .labels = c(count_fraction = \"Total number of patients with at least one adverse event\"), table_names = \"tbl_all\" ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"), .labels = c(count_fraction = \"Total number of patients with fatal AEs\"), table_names = \"tbl_fatal\" ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\", \"AEREL\" = \"Y\"), .labels = c(count_fraction = \"Total number of patients with related fatal AEs\"), .indent_mods = c(count_fraction = 2L), table_names = \"tbl_rel_fatal\" ) build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs 202 177 162 #> Total number of patients with at least one adverse event 59 (100%) 57 (100%) 48 (100%) #> Total number of patients with fatal AEs 28 (47.5%) 31 (54.4%) 20 (41.7%) #> Total number of patients with related fatal AEs 28 (47.5%) 31 (54.4%) 20 (41.7%) # `s_count_patients_with_event()` s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\") ) #> $n #> [1] 164 #> #> $count #> [1] 164 #> #> $count_fraction #> [1] 164 1 #> #> $n_blq #> [1] 0 #> s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\") ) #> $n #> [1] 164 #> #> $count #> [1] 79 #> #> $count_fraction #> [1] 79.0000000 0.4817073 #> #> $n_blq #> [1] 0 #> s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"), denom = \"N_col\", .N_col = 456 ) #> $n #> [1] 164 #> #> $count #> [1] 79 #> #> $count_fraction #> [1] 79.0000000 0.1732456 #> #> $n_blq #> [1] 0 #> # `a_count_patients_with_event()` a_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\"), .N_col = 100, .N_row = 100 ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 164 0 n #> 2 count 164 0 count #> 3 count_fraction 164 (100%) 0 count_fraction #> 4 n_blq 0 0 n_blq"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of patients with particular flags — count_patients_with_flags","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"analyze function count_patients_with_flags() creates layout element calculate counts patients user-specified flags present. function analyzes primary analysis variable var indicates unique subject identifiers. Flags variables analyze specified user via flag_variables argument, must either take value TRUE (flag present) FALSE (flag absent) record. multiple records flag present patient, one occurrence counted.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"","code":"count_patients_with_flags( lyt, var, flag_variables, flag_labels = NULL, var_labels = var, show_labels = \"hidden\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = paste0(\"tbl_flags_\", var), .stats = \"count_fraction\", .formats = NULL, .indent_mods = NULL ) s_count_patients_with_flags( df, .var, flag_variables, flag_labels = NULL, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) a_count_patients_with_flags( df, .var, flag_variables, flag_labels = NULL, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. flag_variables (character) vector specifying names logical variables analysis dataset used counting number unique identifiers. flag_labels (character) vector labels use flag variables. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_patients_with_flags\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) name column contains unique identifier. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"count_patients_with_flags() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_flags() table layout. s_count_patients_with_flags() returns count fraction unique identifiers particular flag list statistics n, count, count_fraction, n_blq, one element per flag. a_count_patients_with_flags() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"count_patients_with_flags(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_patients_with_flags(): Statistics function counts number patients particular flag variable TRUE. a_count_patients_with_flags(): Formatted analysis function used afun count_patients_with_flags().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"flag_labels specified, variables labels extracted df. variables labeled, variable names used instead. Alternatively, named vector can supplied flag_variables within name-value pair name corresponds variable name value label use variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"","code":"library(dplyr) # Add labelled flag variables to analysis dataset. adae <- tern_ex_adae %>% mutate( fl1 = TRUE %>% with_label(\"Total AEs\"), fl2 = (TRTEMFL == \"Y\") %>% with_label(\"Total number of patients with at least one adverse event\"), fl3 = (TRTEMFL == \"Y\" & AEOUT == \"FATAL\") %>% with_label(\"Total number of patients with fatal AEs\"), fl4 = (TRTEMFL == \"Y\" & AEOUT == \"FATAL\" & AEREL == \"Y\") %>% with_label(\"Total number of patients with related fatal AEs\") ) # `count_patients_with_flags()` lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_patients_with_flags( \"SUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"), denom = \"N_col\" ) build_table(lyt2, adae, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs 59 (85.5%) 57 (78.1%) 48 (82.8%) #> Total number of patients with at least one adverse event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> Total number of patients with fatal AEs 28 (40.6%) 31 (42.5%) 20 (34.5%) #> Total number of patients with related fatal AEs 28 (40.6%) 31 (42.5%) 20 (34.5%) # `s_count_patients_with_flags()` s_count_patients_with_flags( adae, \"SUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"), denom = \"N_col\", .N_col = 1000 ) #> $n #> $n$`Total AEs` #> [1] 164 #> #> $n$`Total number of patients with at least one adverse event` #> [1] 164 #> #> $n$`Total number of patients with fatal AEs` #> [1] 164 #> #> $n$`Total number of patients with related fatal AEs` #> [1] 164 #> #> #> $count #> $count$`Total AEs` #> [1] 164 #> #> $count$`Total number of patients with at least one adverse event` #> [1] 164 #> #> $count$`Total number of patients with fatal AEs` #> [1] 79 #> #> $count$`Total number of patients with related fatal AEs` #> [1] 79 #> #> #> $count_fraction #> $count_fraction$`Total AEs` #> [1] 164.000 0.164 #> #> $count_fraction$`Total number of patients with at least one adverse event` #> [1] 164.000 0.164 #> #> $count_fraction$`Total number of patients with fatal AEs` #> [1] 79.000 0.079 #> #> $count_fraction$`Total number of patients with related fatal AEs` #> [1] 79.000 0.079 #> #> #> $n_blq #> $n_blq$`Total AEs` #> [1] 0 #> #> $n_blq$`Total number of patients with at least one adverse event` #> [1] 0 #> #> $n_blq$`Total number of patients with fatal AEs` #> [1] 0 #> #> $n_blq$`Total number of patients with related fatal AEs` #> [1] 0 #> #> # We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. # `a_count_patients_with_flags()` afun <- make_afun(a_count_patients_with_flags, .stats = \"count_fraction\", .ungroup_stats = \"count_fraction\" ) afun( adae, .N_col = 10L, .N_row = 10L, .var = \"USUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\") ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell #> 1 Total AEs 164 (100%) #> 2 Total number of patients with at least one adverse event 164 (100%) #> 3 Total number of patients with fatal AEs 79 (48.2%) #> 4 Total number of patients with related fatal AEs 79 (48.2%) #> indent_mod row_label #> 1 0 Total AEs #> 2 0 Total number of patients with at least one adverse event #> 3 0 Total number of patients with fatal AEs #> 4 0 Total number of patients with related fatal AEs"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Count specific values — count_values","title":"Count specific values — count_values","text":"analyze function count_values() creates layout element calculate counts specific values within variable interest. function analyzes one variables interest supplied vector vars. Values count variable(s) vars can given vector via values argument. One row counts generated variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count specific values — count_values","text":"","code":"count_values( lyt, vars, values, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction\", .formats = NULL, .labels = c(count_fraction = paste(values, collapse = \", \")), .indent_mods = NULL ) s_count_values( x, values, na.rm = TRUE, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) # S3 method for class 'character' s_count_values(x, values = \"Y\", na.rm = TRUE, ...) # S3 method for class 'factor' s_count_values(x, values = \"Y\", ...) # S3 method for class 'logical' s_count_values(x, values = TRUE, ...) a_count_values( x, values, na.rm = TRUE, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count specific values — count_values","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . values (character) specific values counted. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_values\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count specific values — count_values","text":"count_values() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_values() table layout. s_count_values() returns output s_summary() specified values non-numeric variable. a_count_values() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count specific values — count_values","text":"count_values(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_values(): S3 generic function count values. s_count_values(character): Method character class. s_count_values(factor): Method factor class. makes automatic conversion character forwards method characters. s_count_values(logical): Method logical class. a_count_values(): Formatted analysis function used afun count_values().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count specific values — count_values","text":"factor variables, s_count_values checks whether values included levels x fails otherwise. count_values(), variable labels shown one element vars, otherwise hidden.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count specific values — count_values","text":"","code":"# `count_values` basic_table() %>% count_values(\"Species\", values = \"setosa\") %>% build_table(iris) #> all obs #> ———————————————————— #> setosa 50 (33.33%) # `s_count_values.character` s_count_values(x = c(\"a\", \"b\", \"a\"), values = \"a\") #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> s_count_values(x = c(\"a\", \"b\", \"a\", NA, NA), values = \"b\", na.rm = FALSE) #> $n #> [1] 5 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.2 #> #> $n_blq #> [1] 0 #> # `s_count_values.factor` s_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\") #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> # `s_count_values.logical` s_count_values(x = c(TRUE, FALSE, TRUE)) #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> # `a_count_values` a_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\", .N_col = 10, .N_row = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 3 0 n #> 2 count 2 0 count #> 3 count_fraction 2 (66.67%) 0 count_fraction #> 4 n_blq 0 0 n_blq"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox proportional hazards regression — cox_regression","title":"Cox proportional hazards regression — cox_regression","text":"Fits Cox regression model estimates hazard ratio describe effect size survival analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox proportional hazards regression — cox_regression","text":"","code":"summarize_coxreg( lyt, variables, control = control_coxreg(), at = list(), multivar = FALSE, common_var = \"STUDYID\", .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\"), .formats = c(n = \"xx\", hr = \"xx.xx\", ci = \"(xx.xx, xx.xx)\", pval = \"x.xxxx | (<0.0001)\", pval_inter = \"x.xxxx | (<0.0001)\"), varlabels = NULL, .indent_mods = NULL, na_str = \"\", .section_div = NA_character_ ) s_coxreg(model_df, .stats, .which_vars = \"all\", .var_nms = NULL) a_coxreg( df, labelstr, eff = FALSE, var_main = FALSE, multivar = FALSE, variables, at = list(), control = control_coxreg(), .spl_context, .stats, .formats, .indent_mods = NULL, na_str = \"\", cache_env = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox proportional hazards regression — cox_regression","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables. control (list) list parameters returned helper function control_coxreg(). (list numeric) candidate covariate numeric, use specify value covariate effect estimated. multivar (flag) whether multivariate Cox regression run (defaults FALSE), otherwise univariate Cox regression run. common_var (string) name factor variable dataset takes value rows. created pre-processing variable currently exists. .stats (character) names statistics reported among: n: number observations (univariate ) hr: hazard ratio ci: confidence interval pval: p-value treatment effect pval_inter: p-value interaction effect treatment covariate (univariate ) .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. varlabels (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_str (string) custom string replace NA values . Defaults \"\". .section_div (string NA) string repeated section divider sections. Defaults NA section divider. vector two strings given, first used treatment covariate sections second different covariates. model_df (data.frame) contains resulting model fit fit_coxreg function tidying applied via broom::tidy(). .which_vars (character) rows statistics returned given model. Defaults \"\". options include \"var_main\" main effects, \"inter\" interaction effects, \"multi_lvl\" multivariate model covariate level rows. .which_vars \"\", specific variables can selected specifying .var_nms. .var_nms (character) term value rows df .stats returned. Typically name variable. using variable labels, var vector desired variable name variable label order see .stats related variable. .which_vars \"var_main\", .var_nms variable name. df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. eff (flag) whether treatment effect calculated. Defaults FALSE. var_main (flag) whether main effects calculated. Defaults FALSE. .spl_context (data.frame) gives information ancestor split states passed rtables. cache_env (environment) environment object used cache regression model order avoid repeatedly fitting model every row table. Defaults NULL (caching).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox proportional hazards regression — cox_regression","text":"summarize_coxreg() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add Cox regression table containing chosen statistics table layout. s_coxreg() returns selected statistic Cox regression model selected variable(s). a_coxreg() returns formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox proportional hazards regression — cox_regression","text":"Cox models commonly used methods estimate magnitude effect survival analysis. assumes proportional hazards: ratio hazards groups (e.g., two arms) constant time. ratio referred \"hazard ratio\" (HR) one commonly reported metrics describe effect size survival analysis (NEST Team, 2020).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox proportional hazards regression — cox_regression","text":"summarize_coxreg(): Layout-creating function creates Cox regression summary table layout. function wrapper several rtables layouting functions. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups(). s_coxreg(): Statistics function transforms results tabulated fit_coxreg_univar() fit_coxreg_multivar() list. a_coxreg(): Analysis function used afun rtables::analyze() cfun rtables::summarize_row_groups() within summarize_coxreg().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox proportional hazards regression — cox_regression","text":"","code":"library(survival) # Testing dataset [survival::bladder]. set.seed(1, kind = \"Mersenne-Twister\") dta_bladder <- with( data = bladder[bladder$enum < 5, ], tibble::tibble( TIME = stop, STATUS = event, ARM = as.factor(rx), COVAR1 = as.factor(enum) %>% formatters::with_label(\"A Covariate Label\"), COVAR2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) %>% formatters::with_label(\"Sex (F/M)\") ) ) dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) dta_bladder$STUDYID <- factor(\"X\") u1_variables <- list( time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) u2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) m1_variables <- list( time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) m2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) # summarize_coxreg result_univar <- basic_table() %>% summarize_coxreg(variables = u1_variables) %>% build_table(dta_bladder) result_univar #> n Hazard Ratio 95% CI p-value #> ————————————————————————————————————————————————————————————————— #> Treatment: #> 2 vs control (1) 340 0.64 (0.43, 0.94) 0.0242 #> Covariate: #> A Covariate Label 340 0.61 (0.41, 0.90) 0.0126 #> Sex (F/M) 340 0.62 (0.42, 0.92) 0.0182 result_univar_covs <- basic_table() %>% summarize_coxreg( variables = u2_variables, ) %>% build_table(dta_bladder) result_univar_covs #> Hazard Ratio 95% CI p-value #> ——————————————————————————————————————————————————————————————————————————— #> Covariate: #> A Covariate Label (reference = 1) <0.0001 #> 2 0.45 (0.28, 0.71) 0.0007 #> 3 0.31 (0.19, 0.52) <0.0001 #> 4 0.18 (0.10, 0.33) <0.0001 #> Sex (F/M) (reference = F) #> M 1.33 (0.91, 1.94) 0.1414 result_multivar <- basic_table() %>% summarize_coxreg( variables = m1_variables, multivar = TRUE, ) %>% build_table(dta_bladder) result_multivar #> Hazard Ratio 95% CI p-value #> ——————————————————————————————————————————————————————————————————————————— #> Treatment: #> ARM (reference = 1) #> 2 0.61 (0.41, 0.90) 0.0123 #> Covariate: #> A Covariate Label (reference = 1) <0.0001 #> 2 0.46 (0.28, 0.73) 0.0011 #> 3 0.31 (0.18, 0.51) <0.0001 #> 4 0.18 (0.10, 0.33) <0.0001 #> Sex (F/M) (reference = F) #> M 1.29 (0.88, 1.89) 0.1911 result_multivar_covs <- basic_table() %>% summarize_coxreg( variables = m2_variables, multivar = TRUE, varlabels = c(\"Covariate 1\", \"Covariate 2\") # custom labels ) %>% build_table(dta_bladder) result_multivar_covs #> Hazard Ratio 95% CI p-value #> ————————————————————————————————————————————————————————————————————— #> Covariate: #> Covariate 1 (reference = 1) <0.0001 #> 2 0.46 (0.29, 0.74) 0.0012 #> 3 0.31 (0.19, 0.52) <0.0001 #> 4 0.19 (0.10, 0.34) <0.0001 #> Covariate 2 (reference = F) #> M 1.29 (0.88, 1.88) 0.1958 # s_coxreg # Univariate univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder) df1 <- broom::tidy(univar_model) s_coxreg(model_df = df1, .stats = \"hr\") #> $hr #> $hr$`2 vs control (1)` #> [1] 0.6386426 #> #> #> $hr #> $hr$`A Covariate Label` #> [1] 0.607037 #> #> #> $hr #> $hr$`Sex (F/M)` #> [1] 0.6242738 #> #> # Univariate with interactions univar_model_inter <- fit_coxreg_univar( variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder ) df1_inter <- broom::tidy(univar_model_inter) s_coxreg(model_df = df1_inter, .stats = \"hr\", .which_vars = \"inter\", .var_nms = \"COVAR1\") #> $hr #> $hr$` 1` #> [1] 0.6284569 #> #> $hr$` 2` #> [1] 0.5806499 #> #> $hr$` 3` #> [1] 0.5486103 #> #> $hr$` 4` #> [1] 0.6910725 #> #> # Univariate without treatment arm - only \"COVAR2\" covariate effects univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder) df1_covs <- broom::tidy(univar_covs_model) s_coxreg(model_df = df1_covs, .stats = \"hr\", .var_nms = c(\"COVAR2\", \"Sex (F/M)\")) #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #> #> #> $hr #> $hr$M #> [1] 1.3271 #> #> # Multivariate. multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder) df2 <- broom::tidy(multivar_model) s_coxreg(model_df = df2, .stats = \"pval\", .which_vars = \"var_main\", .var_nms = \"COVAR1\") #> $pval #> $pval$`A Covariate Label (reference = 1)` #> [1] 7.209956e-09 #> #> s_coxreg( model_df = df2, .stats = \"pval\", .which_vars = \"multi_lvl\", .var_nms = c(\"COVAR1\", \"A Covariate Label\") ) #> $pval #> $pval$`2` #> [1] 0.001120332 #> #> $pval$`3` #> [1] 6.293725e-06 #> #> $pval$`4` #> [1] 3.013875e-08 #> #> # Multivariate without treatment arm - only \"COVAR1\" main effect multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder) df2_covs <- broom::tidy(multivar_covs_model) s_coxreg(model_df = df2_covs, .stats = \"hr\") #> $hr #> $hr$`2` #> [1] 0.4600728 #> #> $hr$`3` #> [1] 0.3100455 #> #> $hr$`4` #> [1] 0.1854177 #> #> #> $hr #> $hr$`A Covariate Label (reference = 1)` #> numeric(0) #> #> #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #> #> #> $hr #> $hr$M #> [1] 1.285406 #> #> a_coxreg( df = dta_bladder, labelstr = \"Label 1\", variables = u1_variables, .spl_context = list(value = \"COVAR1\"), .stats = \"n\", .formats = \"xx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Label 1 340 0 Label 1 a_coxreg( df = dta_bladder, labelstr = \"\", variables = u1_variables, .spl_context = list(value = \"COVAR2\"), .stats = \"pval\", .formats = \"xx.xxxx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Sex (F/M) 0.0182 0 Sex (F/M)"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox regression helper function for interactions — cox_regression_inter","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Test estimate effect treatment interaction covariate. effect estimated HR tested treatment given level covariate, comparison treatment control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox regression helper function for interactions — cox_regression_inter","text":"","code":"h_coxreg_inter_effect(x, effect, covar, mod, label, control, ...) # S3 method for class 'numeric' h_coxreg_inter_effect(x, effect, covar, mod, label, control, at, ...) # S3 method for class 'factor' h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...) # S3 method for class 'character' h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...) h_coxreg_extract_interaction(effect, covar, mod, data, at, control) h_coxreg_inter_estimations( variable, given, lvl_var, lvl_given, mod, conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox regression helper function for interactions — cox_regression_inter","text":"x (numeric factor) values covariate tested. effect (string) name effect tested estimated. covar (string) name covariate model. mod (coxph) fitted Cox regression model (see survival::coxph()). label (string) label returned term_label. control (list) list controls returned control_coxreg(). ... see methods. (list) list items named covariate, every item vector levels interaction estimated. data (data.frame) data frame model fit. variable, given (string) name variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox regression helper function for interactions — cox_regression_inter","text":"h_coxreg_inter_effect() returns data.frame covariate interaction effects consisting following variables: effect, term, term_label, level, n, hr, lcl, ucl, pval, pval_inter. h_coxreg_extract_interaction() returns result interaction test estimated values. interaction, h_coxreg_univar_extract() applied instead. h_coxreg_inter_estimations() returns list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female) model abbreviated: y ~ Arm + Sex + Arm:Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) estimation Hazard Ratio arm C/sex M given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5). interaction coefficient deduced b2 + b5 standard error obtained $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox regression helper function for interactions — cox_regression_inter","text":"h_coxreg_inter_effect(): S3 generic helper function determine interaction effect. h_coxreg_inter_effect(numeric): Method numeric class. Estimates interaction numeric covariate. h_coxreg_inter_effect(factor): Method factor class. Estimate interaction factor covariate. h_coxreg_inter_effect(character): Method character class. Estimate interaction character covariate. makes automatic conversion factor forwards method factors. h_coxreg_extract_interaction(): higher level function get results interaction test estimated values. h_coxreg_inter_estimations(): Hazard ratio estimation interactions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Automatic conversion character factor guarantee results can generated correctly. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox regression helper function for interactions — cox_regression_inter","text":"","code":"library(survival) set.seed(1, kind = \"Mersenne-Twister\") # Testing dataset [survival::bladder]. dta_bladder <- with( data = bladder[bladder$enum < 5, ], data.frame( time = stop, status = event, armcd = as.factor(rx), covar1 = as.factor(enum), covar2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) plot( survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder), lty = 2:4, xlab = \"Months\", col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") ) mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) h_coxreg_extract_interaction( mod = mod, effect = \"armcd\", covar = \"covar1\", data = dta_bladder, control = control_coxreg() ) #> effect term term_label level n hr #> 1 Covariate: covar1 A Covariate Label 340 NA #> armcd2/covar11 Covariate: covar1 1 1 NA 0.6341111 #> armcd2/covar12 Covariate: covar1 2 2 NA 0.5845305 #> armcd2/covar13 Covariate: covar1 3 3 NA 0.5507703 #> armcd2/covar14 Covariate: covar1 4 4 NA 0.6910643 #> lcl ucl pval pval_inter #> 1 NA NA 0.1302825 0.988245 #> armcd2/covar11 0.3514676 1.144051 NA NA #> armcd2/covar12 0.2716689 1.257692 NA NA #> armcd2/covar13 0.2244668 1.351415 NA NA #> armcd2/covar14 0.2315248 2.062715 NA NA mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) result <- h_coxreg_inter_estimations( variable = \"armcd\", given = \"covar1\", lvl_var = levels(dta_bladder$armcd), lvl_given = levels(dta_bladder$covar1), mod = mod, conf_level = .95 ) result #> $armcd2 #> coef se(coef) hr lcl ucl #> armcd2/covar11 -0.4555312 0.3010803 0.6341111 0.3514676 1.144051 #> armcd2/covar12 -0.5369464 0.3909383 0.5845305 0.2716689 1.257692 #> armcd2/covar13 -0.5964375 0.4579624 0.5507703 0.2244668 1.351415 #> armcd2/covar14 -0.3695225 0.5579418 0.6910643 0.2315248 2.062715 #> #> attr(,\"details\") #> [1] \"Estimations of armcd hazard ratio given the level of covar1 compared to armcd level 1.\""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":null,"dir":"Reference","previous_headings":"","what":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"cuts numeric vector sample quantile bins.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"","code":"cut_quantile_bins( x, probs = c(0.25, 0.5, 0.75), labels = NULL, type = 7, ordered = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"x (numeric) continuous variable values cut quantile bins. may contain NA values, used quantile calculations, included return vector. probs (numeric) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. labels (character) unique labels quantile bins. n probabilities probs, must n + 1 long. type (integer(1)) type quantiles use, see stats::quantile() details. ordered (flag) result ordered factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"factor variable appropriately-labeled bins levels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"Intervals closed right side. , first bin interval [-Inf, q1] q1 first quantile, second bin (q1, q2], etc., last bin (qn, +Inf] qn last quantile.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"","code":"# Default is to cut into quartile bins. cut_quantile_bins(cars$speed) #> [1] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] #> [7] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] #> [13] [0%,25%] [0%,25%] [0%,25%] (25%,50%] (25%,50%] (25%,50%] #> [19] (25%,50%] (25%,50%] (25%,50%] (25%,50%] (25%,50%] (25%,50%] #> [25] (25%,50%] (25%,50%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] #> [31] (50%,75%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] #> [37] (50%,75%] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [43] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [49] (75%,100%] (75%,100%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] # Use custom quantiles. cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88)) #> [1] [0%,10%] [0%,10%] [0%,10%] [0%,10%] [0%,10%] (10%,20%] #> [7] (10%,20%] (10%,20%] (10%,20%] (10%,20%] (10%,20%] (20%,60%] #> [13] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [19] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [25] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [31] (20%,60%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] #> [37] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] #> [43] (60%,88%] (60%,88%] (88%,100%] (88%,100%] (88%,100%] (88%,100%] #> [49] (88%,100%] (88%,100%] #> Levels: [0%,10%] < (10%,20%] < (20%,60%] < (60%,88%] < (88%,100%] # Use custom labels. cut_quantile_bins(cars$speed, labels = paste0(\"Q\", 1:4)) #> [1] Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 #> [26] Q2 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 #> Levels: Q1 < Q2 < Q3 < Q4 # NAs are preserved in result factor. ozone_binned <- cut_quantile_bins(airquality$Ozone) which(is.na(ozone_binned)) #> [1] 5 10 25 26 27 32 33 34 35 36 37 39 42 43 45 46 52 53 54 #> [20] 55 56 57 58 59 60 61 65 72 75 83 84 102 103 107 115 119 150 # So you might want to make these explicit. explicit_na(ozone_binned) #> [1] (50%,75%] (50%,75%] [0%,25%] [0%,25%] (25%,50%] #> [7] (25%,50%] (25%,50%] [0%,25%] [0%,25%] [0%,25%] #> [13] [0%,25%] [0%,25%] [0%,25%] [0%,25%] (50%,75%] [0%,25%] #> [19] (25%,50%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] (50%,75%] #> [25] (25%,50%] (50%,75%] (75%,100%] #> [31] (50%,75%] #> [37] (25%,50%] (75%,100%] (50%,75%] #> [43] (25%,50%] (25%,50%] (50%,75%] #> [49] (25%,50%] [0%,25%] [0%,25%] #> [55] #> [61] (75%,100%] (50%,75%] (50%,75%] (75%,100%] #> [67] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [73] [0%,25%] (25%,50%] [0%,25%] (50%,75%] (50%,75%] #> [79] (50%,75%] (75%,100%] (50%,75%] [0%,25%] #> [85] (75%,100%] (75%,100%] (25%,50%] (50%,75%] (75%,100%] (50%,75%] #> [91] (75%,100%] (50%,75%] (50%,75%] [0%,25%] [0%,25%] (75%,100%] #> [97] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [103] (50%,75%] (25%,50%] (75%,100%] (25%,50%] #> [109] (50%,75%] (25%,50%] (25%,50%] (50%,75%] (25%,50%] [0%,25%] #> [115] (50%,75%] (75%,100%] (75%,100%] (75%,100%] #> [121] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [127] (75%,100%] (50%,75%] (50%,75%] (25%,50%] (25%,50%] (25%,50%] #> [133] (25%,50%] (50%,75%] (25%,50%] (25%,50%] [0%,25%] [0%,25%] #> [139] (50%,75%] [0%,25%] [0%,25%] (25%,50%] [0%,25%] [0%,25%] #> [145] (25%,50%] (50%,75%] [0%,25%] [0%,25%] (25%,50%] #> [151] [0%,25%] [0%,25%] (25%,50%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] < "},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Description function produces labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(abnormal)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"abnormal (character) values identifying abnormal range level(s) .var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Abnormal category labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(\"LOW\") #> $not_abnormal #> [1] \"Not low\" #> #> $abnormal #> [1] \"Low\" #> #> $total #> [1] \"Total\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of cumulative count — d_count_cumulative","title":"Description of cumulative count — d_count_cumulative","text":"helper function describes analysis s_count_cumulative().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of cumulative count — d_count_cumulative","text":"","code":"d_count_cumulative(threshold, lower_tail = TRUE, include_eq = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of cumulative count — d_count_cumulative","text":"threshold (numeric(1)) cutoff value threshold count values x. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of cumulative count — d_count_cumulative","text":"Labels s_count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"","code":"d_count_missed_doses(thresholds)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"thresholds (numeric) minimum number missed doses patients .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"d_count_missed_doses() returns named character vector labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of standard oncology response — d_onco_rsp_label","title":"Description of standard oncology response — d_onco_rsp_label","text":"Describe oncology response standard way.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of standard oncology response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of standard oncology response — d_onco_rsp_label","text":"x (character) standard oncology codes described.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of standard oncology response — d_onco_rsp_label","text":"Response labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of standard oncology response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label( c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\", \"Missing\", \"\", \"NE/Missing\") ) #> CR PR #> Complete Response (CR) Partial Response (PR) #> SD NON CR/PD #> Stable Disease (SD) Non-CR or Non-PD (NON CR/PD) #> PD NE #> Progressive Disease (PD) Not Evaluable (NE) #> Missing #> Missing #> NE/Missing #> Missing or unevaluable #> 9 Levels: Complete Response (CR) Partial Response (PR) ... # Adding some values not considered in d_onco_rsp_label d_onco_rsp_label( c(\"CR\", \"PR\", \"hello\", \"hi\") ) #> CR PR hello #> Complete Response (CR) Partial Response (PR) hello #> hi #> hi #> Levels: Complete Response (CR) Partial Response (PR) hello hi"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PK reference dataset — d_pkparam","text":"","code":"d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PK reference dataset — d_pkparam","text":"data.frame PK parameters.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate PK reference dataset — d_pkparam","text":"","code":"pk_reference_dataset <- d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the proportion summary — d_proportion","title":"Description of the proportion summary — d_proportion","text":"helper function describes analysis s_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the proportion summary — d_proportion","text":"","code":"d_proportion(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the proportion summary — d_proportion","text":"conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. long (flag) whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the proportion summary — d_proportion","text":"String describing analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of method used for proportion comparison — d_proportion_diff","title":"Description of method used for proportion comparison — d_proportion_diff","text":"auxiliary function describes analysis s_proportion_diff().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of method used for proportion comparison — d_proportion_diff","text":"","code":"d_proportion_diff(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of method used for proportion comparison — d_proportion_diff","text":"conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. long (flag) whether long (TRUE) short (FALSE, default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of method used for proportion comparison — d_proportion_diff","text":"string describing analysis.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"Internal function check variables included tabulate_rsp_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"","code":"d_rsp_subgroups_colvars(vars, conf_level = NULL, method = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"list variables tabulate labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"Internal function check variables included tabulate_survival_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"","code":"d_survival_subgroups_colvars(vars, conf_level, method, time_unit = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"vars (character) names statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. conf_level (proportion) confidence level interval. method (string) p-value method testing hazard ratio = 1. time_unit (string) label unit median survival time. Default NULL skips displaying unit.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"list variables labels tabulate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"least one n_tot n_tot_events must provided vars.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the difference test between two proportions — d_test_proportion_diff","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"auxiliary function describes analysis s_test_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"","code":"d_test_proportion_diff(method)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"string describing test p-value derived.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of days to months — day2month","title":"Conversion of days to months — day2month","text":"Conversion days months","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of days to months — day2month","text":"","code":"day2month(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of days to months — day2month","text":"x (numeric(1)) time days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of days to months — day2month","text":"numeric vector time months.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of days to months — day2month","text":"","code":"x <- c(403, 248, 30, 86) day2month(x) #> [1] 13.2402464 8.1478439 0.9856263 2.8254620"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"function useful label grid grobs (also ggplot2, lattice plots) title, footnote, page numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"","code":"decorate_grob( grob, titles, footnotes, page = \"\", width_titles = grid::unit(1, \"npc\"), width_footnotes = grid::unit(1, \"npc\"), border = TRUE, padding = grid::unit(rep(1, 4), \"lines\"), margins = grid::unit(c(1, 0, 1, 0), \"lines\"), outer_margins = grid::unit(c(2, 1.5, 3, 1.5), \"cm\"), gp_titles = grid::gpar(), gp_footnotes = grid::gpar(fontsize = 8), name = NULL, gp = grid::gpar(), vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"grob (grob) grid grob object, optionally NULL grob decoration shown. titles (character) titles given vector strings separated newline wrapped according page width. footnotes (character) footnotes. Uses formatting rules titles. page (string NULL) page numeration. NULL page number displayed. width_titles (grid::unit) width titles. Usually defined available space grid::unit(1, \"npc\"), affected parameter outer_margins. Right margins (outer_margins[4]) need subtracted allowed width. width_footnotes (grid::unit) width footnotes. default margin correction width_titles. border (flag) whether border drawn around plot . padding (grid::unit) padding. unit object length 4. Innermost margin plot (grob) , possibly, border plot. Usually expressed 4 identical values (usually \"lines\"). defaults grid::unit(rep(1, 4), \"lines\"). margins (grid::unit) margins. unit object length 4. Margins plot elements list (e.g. titles, plot, footers). usually expressed 4 \"lines\", lateral ones 0s, top bottom 1s. defaults grid::unit(c(1, 0, 1, 0), \"lines\"). outer_margins (grid::unit) outer margins. unit object length 4. defines general margin plot, considering also decorations like titles, footnotes, page numbers. defaults grid::unit(c(2, 1.5, 3, 1.5), \"cm\"). gp_titles (gpar) gpar object. Mainly used set different \"fontsize\". gp_footnotes (gpar) gpar object. Mainly used set different \"fontsize\". name character identifier grob. Used find grob display list /child another grob. gp \"gpar\" object, typically output call function gpar. basically list graphical parameter settings. vp viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"grid grob (gTree).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"titles footnotes ragged, .e. title wrapped individually.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"","code":"library(grid) titles <- c( \"Edgar Anderson's Iris Data\", paste( \"This famous (Fisher's or Anderson's) iris data set gives the measurements\", \"in centimeters of the variables sepal length and width and petal length\", \"and width, respectively, for 50 flowers from each of 3 species of iris.\" ) ) footnotes <- c( \"The species are Iris setosa, versicolor, and virginica.\", paste( \"iris is a data frame with 150 cases (rows) and 5 variables (columns) named\", \"Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.\" ) ) ## empty plot grid.newpage() grid.draw( decorate_grob( NULL, titles = titles, footnotes = footnotes, page = \"Page 4 of 10\" ) ) # grid p <- gTree( children = gList( rectGrob(), xaxisGrob(), yaxisGrob(), textGrob(\"Sepal.Length\", y = unit(-4, \"lines\")), textGrob(\"Petal.Length\", x = unit(-3.5, \"lines\"), rot = 90), pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16) ), vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length)) ) grid.newpage() grid.draw(p) grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) ## with ggplot2 library(ggplot2) p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) + ggplot2::geom_point() p_gg p <- ggplotGrob(p_gg) grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) ## with lattice library(lattice) xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species) p <- grid.grab() grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) # with gridExtra - no borders library(gridExtra) #> #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #> #> combine grid.newpage() grid.draw( decorate_grob( tableGrob( head(mtcars) ), titles = \"title\", footnotes = \"footnote\", border = FALSE ) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Update page number — decorate_grob_factory","title":"Update page number — decorate_grob_factory","text":"Automatically updates page number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update page number — decorate_grob_factory","text":"","code":"decorate_grob_factory(npages, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update page number — decorate_grob_factory","text":"npages (numeric(1)) total number pages. ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update page number — decorate_grob_factory","text":"Closure increments page number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorate set of grobs and add page numbering — decorate_grob_set","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"Note uses decorate_grob_factory() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"","code":"decorate_grob_set(grobs, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"grobs (list grob) list grid grobs. ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"decorated grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"","code":"library(ggplot2) library(grid) g <- with(data = iris, { list( ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) + ggplot2::geom_point() ) ) }) lg <- decorate_grob_set(grobs = g, titles = \"Hello\\nOne\\nTwo\\nThree\", footnotes = \"\") draw_grob(lg[[1]]) #> Warning: `draw_grob()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. draw_grob(lg[[2]]) draw_grob(lg[[6]])"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":null,"dir":"Reference","previous_headings":"","what":"Default string replacement for NA values — default_na_str","title":"Default string replacement for NA values — default_na_str","text":"default string used represent NA values. value used default value na_str argument throughout tern package, printed place NA values output tables. specified tern function user via na_str argument, R environment options via set_default_na_str(), NA used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default string replacement for NA values — default_na_str","text":"","code":"default_na_str() set_default_na_str(na_str)"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default string replacement for NA values — default_na_str","text":"na_str (string) single string value set R environment options default value replace NAs. Use getOption(\"tern_default_na_str\") check current value set R environment (defaults NULL set).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default string replacement for NA values — default_na_str","text":"default_na_str returns current value R environment option set \"tern_default_na_str\", NA_character_ otherwise. set_default_na_str return value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Default string replacement for NA values — default_na_str","text":"default_na_str(): Accessor default NA value replacement string. set_default_na_str(): Setter default NA value replacement string. Sets option \"tern_default_na_str\" within R environment.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default string replacement for NA values — default_na_str","text":"","code":"# Default settings default_na_str() #> [1] NA getOption(\"tern_default_na_str\") #> NULL # Set custom value set_default_na_str(\"\") # Settings after value has been set default_na_str() #> [1] \"\" getOption(\"tern_default_na_str\") #> [1] \"\""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"Utility functions get valid statistic methods different method groups (.stats) associated formats (.formats), labels (.labels), indent modifiers (.indent_mods). utility used across tern, working principles can seen analyze_vars(). See notes understand experimental.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"","code":"get_stats( method_groups = \"analyze_vars_numeric\", stats_in = NULL, add_pval = FALSE ) get_formats_from_stats(stats, formats_in = NULL) get_labels_from_stats(stats, labels_in = NULL, row_nms = NULL) get_indents_from_stats(stats, indents_in = NULL, row_nms = NULL) tern_default_stats tern_default_formats tern_default_labels summary_formats(type = \"numeric\", include_pval = FALSE) summary_labels(type = \"numeric\", include_pval = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"tern_default_stats named list available statistics, element named corresponding statistical method group. tern_default_formats named vector available default formats, element named corresponding statistic. tern_default_labels named character vector available default labels, element named corresponding statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"method_groups (character) indicates statistical method group (tern analyze function) retrieve default statistics . character vector can used specify one statistical method group. stats_in (character) statistics retrieve selected method group. add_pval (flag) \"pval\" (\"pval_counts\" method_groups contains \"analyze_vars_counts\") added statistical methods? stats (character) statistical methods get defaults . formats_in (named vector) inserted formats replace defaults. can character vector formatters::list_valid_format_labels() custom format function. labels_in (named character) inserted labels replace defaults. row_nms (character) row names. Levels factor character variable, statistics .stats calculated . parameter set, variable levels used defaults, names given custom values correspond levels (format statistic.level) instead statistics. Can also variable names rows correspond different variables instead levels. Defaults NULL. indents_in (named vector) inserted indent modifiers replace defaults (default 0L). type (string)\"numeric\" \"counts\". include_pval (flag) add_pval argument get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"get_stats() returns character vector statistical methods. get_formats_from_stats() returns named vector formats (present either tern_default_formats formats_in, otherwise NULL). Values can taken formatters::list_valid_format_labels() custom function (e.g. formatting_functions). get_labels_from_stats() returns named character vector labels (present either tern_default_labels labels_in, otherwise NULL). get_indents_from_stats() returns single indent modifier value apply rows named numeric vector indent modifiers (present, otherwise NULL). summary_formats() returns named vector default statistic formats given data type. summary_labels returns named vector default statistic labels given data type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"Current choices type counts numeric analyze_vars() affect get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"get_stats(): Get statistics available given method group (analyze function). check available defaults see tern::tern_default_stats list. get_formats_from_stats(): Get formats corresponding list statistics. check available defaults see tern::tern_default_formats list. get_labels_from_stats(): Get labels corresponding list statistics. check available defaults see tern::tern_default_labels list. available , statistics name used label. get_indents_from_stats(): Format indent modifiers given vector/list statistics. defaults 0L values. tern_default_stats: Named list available statistics method group tern. tern_default_formats: Named vector default formats tern. tern_default_labels: Named character vector default labels tern. summary_formats(): Quick function retrieve default formats summary statistics: analyze_vars() analyze_vars_in_cols() principally. summary_labels(): Quick function retrieve default labels summary statistics. Returns labels descriptive statistics understood rtables. Similar summary_formats.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"defaults experimental use names functions retrieve default statistics. generalized groups methods according reasonable groupings. Formats tern rtables can functions take table cell value return string. well documented vignette(\"custom_appearance\", package = \"rtables\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"","code":"# analyze_vars is numeric num_stats <- get_stats(\"analyze_vars_numeric\") # also the default # Other type cnt_stats <- get_stats(\"analyze_vars_counts\") # Weirdly taking the pval from count_occurrences only_pval <- get_stats(\"count_occurrences\", add_pval = TRUE, stats_in = \"pval\") # All count_occurrences all_cnt_occ <- get_stats(\"count_occurrences\") # Multiple get_stats(c(\"count_occurrences\", \"analyze_vars_counts\")) #> [1] \"count\" \"count_fraction\" #> [3] \"count_fraction_fixed_dp\" \"fraction\" #> [5] \"n\" \"n_blq\" # Defaults formats get_formats_from_stats(num_stats) #> $n #> [1] \"xx.\" #> #> $sum #> [1] \"xx.x\" #> #> $mean #> [1] \"xx.x\" #> #> $sd #> [1] \"xx.x\" #> #> $se #> [1] \"xx.x\" #> #> $mean_sd #> [1] \"xx.x (xx.x)\" #> #> $mean_se #> [1] \"xx.x (xx.x)\" #> #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_pval #> [1] \"x.xxxx | (<0.0001)\" #> #> $median #> [1] \"xx.x\" #> #> $mad #> [1] \"xx.x\" #> #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $quantiles #> [1] \"xx.x - xx.x\" #> #> $iqr #> [1] \"xx.x\" #> #> $range #> [1] \"xx.x - xx.x\" #> #> $min #> [1] \"xx.x\" #> #> $max #> [1] \"xx.x\" #> #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #> #> $cv #> [1] \"xx.x\" #> #> $geom_mean #> [1] \"xx.x\" #> #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $geom_cv #> [1] \"xx.x\" #> get_formats_from_stats(cnt_stats) #> $n #> [1] \"xx.\" #> #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> #> $n_blq #> [1] \"xx.\" #> get_formats_from_stats(only_pval) #> $pval #> [1] \"x.xxxx | (<0.0001)\" #> get_formats_from_stats(all_cnt_occ) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> # Addition of customs get_formats_from_stats(all_cnt_occ, formats_in = c(\"fraction\" = c(\"xx\"))) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> [1] \"xx\" #> get_formats_from_stats(all_cnt_occ, formats_in = list(\"fraction\" = c(\"xx.xx\", \"xx\"))) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> [1] \"xx.xx\" \"xx\" #> # Defaults labels get_labels_from_stats(num_stats) #> n sum #> \"n\" \"Sum\" #> mean sd #> \"Mean\" \"SD\" #> se mean_sd #> \"SE\" \"Mean (SD)\" #> mean_se mean_ci #> \"Mean (SE)\" \"Mean 95% CI\" #> mean_sei mean_sdi #> \"Mean -/+ 1xSE\" \"Mean -/+ 1xSD\" #> mean_pval median #> \"Mean p-value (H0: mean = 0)\" \"Median\" #> mad median_ci #> \"Median Absolute Deviation\" \"Median 95% CI\" #> quantiles iqr #> \"25% and 75%-ile\" \"IQR\" #> range min #> \"Min - Max\" \"Minimum\" #> max median_range #> \"Maximum\" \"Median (Min - Max)\" #> cv geom_mean #> \"CV (%)\" \"Geometric Mean\" #> geom_mean_ci geom_cv #> \"Geometric Mean 95% CI\" \"CV % Geometric Mean\" get_labels_from_stats(cnt_stats) #> n count count_fraction #> \"n\" \"count\" \"count_fraction\" #> count_fraction_fixed_dp fraction n_blq #> \"count_fraction\" \"fraction\" \"n_blq\" get_labels_from_stats(only_pval) #> pval #> \"p-value (t-test)\" get_labels_from_stats(all_cnt_occ) #> count count_fraction count_fraction_fixed_dp #> \"count\" \"count_fraction\" \"count_fraction\" #> fraction #> \"fraction\" # Addition of customs get_labels_from_stats(all_cnt_occ, labels_in = c(\"fraction\" = \"Fraction\")) #> count count_fraction count_fraction_fixed_dp #> \"count\" \"count_fraction\" \"count_fraction\" #> fraction #> \"Fraction\" get_labels_from_stats(all_cnt_occ, labels_in = list(\"fraction\" = c(\"Some more fractions\"))) #> $count #> [1] \"count\" #> #> $count_fraction #> [1] \"count_fraction\" #> #> $count_fraction_fixed_dp #> [1] \"count_fraction\" #> #> $fraction #> [1] \"Some more fractions\" #> get_indents_from_stats(all_cnt_occ, indents_in = 3L) #> [1] 3 3 3 3 get_indents_from_stats(all_cnt_occ, indents_in = list(count = 2L, count_fraction = 5L)) #> $count #> [1] 2 #> #> $count_fraction #> [1] 5 #> #> $count_fraction_fixed_dp #> [1] 0 #> #> $fraction #> [1] 0 #> get_indents_from_stats( all_cnt_occ, indents_in = list(a = 2L, count.a = 1L, count.b = 5L), row_nms = c(\"a\", \"b\") ) #> $count.a #> [1] 1 #> #> $count.b #> [1] 5 #> #> $count_fraction.a #> [1] 2 #> #> $count_fraction.b #> [1] 0 #> #> $count_fraction_fixed_dp.a #> [1] 2 #> #> $count_fraction_fixed_dp.b #> [1] 0 #> #> $fraction.a #> [1] 2 #> #> $fraction.b #> [1] 0 #> summary_formats() #> Warning: `summary_formats()` was deprecated in tern 0.9.6. #> ℹ Use get_formats_from_stats(get_stats(\"analyze_vars_numeric\", add_pval = #> include_pval)) instead #> $n #> [1] \"xx.\" #> #> $sum #> [1] \"xx.x\" #> #> $mean #> [1] \"xx.x\" #> #> $sd #> [1] \"xx.x\" #> #> $se #> [1] \"xx.x\" #> #> $mean_sd #> [1] \"xx.x (xx.x)\" #> #> $mean_se #> [1] \"xx.x (xx.x)\" #> #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_pval #> [1] \"x.xxxx | (<0.0001)\" #> #> $median #> [1] \"xx.x\" #> #> $mad #> [1] \"xx.x\" #> #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $quantiles #> [1] \"xx.x - xx.x\" #> #> $iqr #> [1] \"xx.x\" #> #> $range #> [1] \"xx.x - xx.x\" #> #> $min #> [1] \"xx.x\" #> #> $max #> [1] \"xx.x\" #> #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #> #> $cv #> [1] \"xx.x\" #> #> $geom_mean #> [1] \"xx.x\" #> #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $geom_cv #> [1] \"xx.x\" #> summary_formats(type = \"counts\", include_pval = TRUE) #> $n #> [1] \"xx.\" #> #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> #> $n_blq #> [1] \"xx.\" #> #> $pval_counts #> [1] \"x.xxxx | (<0.0001)\" #> summary_labels() #> Warning: `summary_formats()` was deprecated in tern 0.9.6. #> ℹ Use get_labels_from_stats(get_stats(\"analyze_vars_numeric\", add_pval = #> include_pval)) instead #> n sum #> \"n\" \"Sum\" #> mean sd #> \"Mean\" \"SD\" #> se mean_sd #> \"SE\" \"Mean (SD)\" #> mean_se mean_ci #> \"Mean (SE)\" \"Mean 95% CI\" #> mean_sei mean_sdi #> \"Mean -/+ 1xSE\" \"Mean -/+ 1xSD\" #> mean_pval median #> \"Mean p-value (H0: mean = 0)\" \"Median\" #> mad median_ci #> \"Median Absolute Deviation\" \"Median 95% CI\" #> quantiles iqr #> \"25% and 75%-ile\" \"IQR\" #> range min #> \"Min - Max\" \"Minimum\" #> max median_range #> \"Maximum\" \"Median (Min - Max)\" #> cv geom_mean #> \"CV (%)\" \"Geometric Mean\" #> geom_mean_ci geom_cv #> \"Geometric Mean 95% CI\" \"CV % Geometric Mean\" summary_labels(type = \"counts\", include_pval = TRUE) #> n count #> \"n\" \"count\" #> count_fraction count_fraction_fixed_dp #> \"count_fraction\" \"count_fraction\" #> fraction n_blq #> \"fraction\" \"n_blq\" #> pval_counts #> \"p-value (chi-squared test)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for a difference of binomials — desctools_binom","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"Several confidence intervals difference proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"","code":"desctools_binom( x1, n1, x2, n2, conf.level = 0.95, sides = c(\"two.sided\", \"left\", \"right\"), method = c(\"ac\", \"wald\", \"waldcc\", \"score\", \"scorecc\", \"mn\", \"mee\", \"blj\", \"ha\", \"hal\", \"jp\") ) desctools_binomci( x, n, conf.level = 0.95, sides = c(\"two.sided\", \"left\", \"right\"), method = c(\"wilson\", \"wald\", \"waldcc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"wilsoncc\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\"), rand = 123, tol = 1e-05 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"conf.level (proportion) confidence level, defaults 0.95. sides (string) side confidence interval compute. Must one \"two-sided\" (default), \"left\", \"right\". method (string) method use. Can one : \"wald\", \"wilson\", \"wilsoncc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\". x (integer(1)) number successes. n (integer(1)) number trials.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"matrix 3 values: est: estimate proportion difference. lwr.ci: estimate lower end confidence interval. upr.ci: estimate upper end confidence interval. matrix 3 columns containing: est: estimate proportion difference. lwr.ci: lower end confidence interval. upr.ci: upper end confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"desctools_binom(): Several confidence intervals difference proportions. desctools_binomci(): Compute confidence intervals binomial proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data.frame object to ggplot object — df2gg","title":"Convert data.frame object to ggplot object — df2gg","text":"Given data.frame object, performs basic conversion ggplot2::ggplot() object built using functions ggplot2 package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data.frame object to ggplot object — df2gg","text":"","code":"df2gg( df, colwidths = NULL, font_size = 10, col_labels = TRUE, col_lab_fontface = \"bold\", hline = TRUE, bg_fill = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data.frame object to ggplot object — df2gg","text":"df (data.frame) data frame. colwidths (numeric NULL) vector column widths. element's position colwidths corresponds column df position. NULL, column widths calculated according maximum number characters per column. font_size (numeric(1)) font size. col_labels (flag) whether column names (labels) df used first row output table. col_lab_fontface (string) font face apply first row (column labels col_labels = TRUE). Defaults \"bold\". hline (flag) whether horizontal line printed first row table. bg_fill (string) table background fill color.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data.frame object to ggplot object — df2gg","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data.frame object to ggplot object — df2gg","text":"","code":"if (FALSE) { # \\dontrun{ df2gg(head(iris, 5)) df2gg(head(iris, 5), font_size = 15, colwidths = c(1, 1, 1, 1, 1)) } # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode categorical missing values in a data frame — df_explicit_na","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"helper function encode missing entries across groups categorical variables data frame.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"","code":"df_explicit_na( data, omit_columns = NULL, char_as_factor = TRUE, logical_as_factor = FALSE, na_level = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"data (data.frame) data set. omit_columns (character) names variables data modified function. char_as_factor (flag) whether convert character variables data factors. logical_as_factor (flag) whether convert logical variables data factors. na_level (string) string used replace NA empty values inside non-omit_columns columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"data.frame chosen modifications applied.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"Missing entries NA empty strings replaced specified value. factor variables include missing values, missing value inserted last level. Similarly, case character logical variables converted factors char_as_factor logical_as_factor options, missing values set last level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"","code":"my_data <- data.frame( u = c(TRUE, FALSE, NA, TRUE), v = factor(c(\"A\", NA, NA, NA), levels = c(\"Z\", \"A\")), w = c(\"A\", \"B\", NA, \"C\"), x = c(\"D\", \"E\", \"F\", NA), y = c(\"G\", \"H\", \"I\", \"\"), z = c(1, 2, 3, 4), stringsAsFactors = FALSE ) # Example 1 # Encode missing values in all character or factor columns. df_explicit_na(my_data) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 NA F I 3 #> 4 TRUE C 4 # Also convert logical columns to factor columns. df_explicit_na(my_data, logical_as_factor = TRUE) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 F I 3 #> 4 TRUE C 4 # Encode missing values in a subset of columns. df_explicit_na(my_data, omit_columns = c(\"x\", \"y\")) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 NA F I 3 #> 4 TRUE C 4 # Example 2 # Here we purposefully convert all `M` values to `NA` in the `SEX` variable. # After running `df_explicit_na` the `NA` values are encoded as `` but they are not # included when generating `rtables`. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl) # If you want the `Na` values to be displayed in the table use the `na_level` argument. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\") # Example 3 # Numeric variables that have missing values are not altered. This means that any `NA` value in # a numeric variable will not be included in the summary statistics, nor will they be included # in the denominator value for calculating the percent values. adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)"},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to check if a float value is equal to another float value — .is_equal_float","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"Uses .Machine$double.eps tolerance comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"","code":".is_equal_float(x, y)"},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"x (numeric(1)) float number. y (numeric(1)) float number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"TRUE identical, otherwise FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw grob — draw_grob","title":"Draw grob — draw_grob","text":"Draw grob device page.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw grob — draw_grob","text":"","code":"draw_grob(grob, newpage = TRUE, vp = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw grob — draw_grob","text":"grob (grob) grid object. newpage (flag) draw new page. vp (viewport NULL) viewport() object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw grob — draw_grob","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw grob — draw_grob","text":"","code":"library(dplyr) library(grid) # \\donttest{ rect <- rectGrob(width = grid::unit(0.5, \"npc\"), height = grid::unit(0.5, \"npc\")) rect %>% draw_grob(vp = grid::viewport(angle = 45)) num <- lapply(1:10, textGrob) num %>% arrange_grobs(grobs = .) %>% draw_grob() #> Warning: `stack_grobs()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. #> ℹ The deprecated feature was likely used in the tern package. #> Please report the issue at #> . showViewport() # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Return an empty numeric if all elements are NA. — empty_vector_if_na","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"Return empty numeric elements NA.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"empty_vector_if_na(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"x (numeric) vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"empty numeric elements x NA, otherwise x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"x <- c(NA, NA, NA) # Internal function - empty_vector_if_na"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Hazard ratio estimation in interactions — estimate_coef","title":"Hazard ratio estimation in interactions — estimate_coef","text":"function estimates hazard ratios arms interaction variable given specific values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hazard ratio estimation in interactions — estimate_coef","text":"","code":"estimate_coef( variable, given, lvl_var, lvl_given, coef, mmat, vcov, conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hazard ratio estimation in interactions — estimate_coef","text":"variable, given (character(2)) names two variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). coef (numeric) vector estimated coefficients. mmat (named numeric) vector filled 0s used template obtain design matrix. vcov (matrix) variance-covariance matrix underlying model. conf_level (proportion) confidence level estimate intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hazard ratio estimation in interactions — estimate_coef","text":"list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hazard ratio estimation in interactions — estimate_coef","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female). model abbreviated: y ~ Arm + Sex + Arm x Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) Given want estimation Hazard Ratio arm C/sex M, estimation given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5), therefore interaction coefficient given b2 + b5 standard error obtained $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ confidence level 0.95.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hazard ratio estimation in interactions — estimate_coef","text":"","code":"library(dplyr) library(survival) ADSL <- tern_ex_adsl %>% filter(SEX %in% c(\"F\", \"M\")) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"PFS\") adtte$ARMCD <- droplevels(adtte$ARMCD) adtte$SEX <- droplevels(adtte$SEX) mod <- coxph( formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2, data = adtte ) mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"analyze & summarize function estimate_multinomial_response() creates layout element estimate proportion proportion confidence interval level factor variable. primary analysis variable, var, factor variable, values used labels within output table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"","code":"estimate_multinomial_response( lyt, var, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = var, .stats = \"prop_ci\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_length_proportion(x, .N_col, ...) a_length_proportion(x, .N_col, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_multinomial_response\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"estimate_multinomial_response() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_length_proportion() table layout. s_length_proportion() returns statistics s_proportion(). a_length_proportion() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"estimate_multinomial_response(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze() rtables::summarize_row_groups(). s_length_proportion(): Statistics function feeds length x number successes, .N_col total number successes failures s_proportion(). a_length_proportion(): Formatted analysis function used afun estimate_multinomial_response().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"","code":"library(dplyr) # Use of the layout creating function. dta_test <- data.frame( USUBJID = paste0(\"S\", 1:12), ARM = factor(rep(LETTERS[1:3], each = 4)), AVAL = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) ) %>% mutate( AVALC = factor(AVAL, levels = c(0, 1), labels = c(\"Complete Response (CR)\", \"Partial Response (PR)\") ) ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% estimate_multinomial_response(var = \"AVALC\") tbl <- build_table(lyt, dta_test) tbl #> A B C #> ————————————————————————————————————————————————————————————————————————————————————— #> Complete Response (CR) 0 (0.0%) 2 (50.0%) 4 (100.0%) #> 95% CI (Wald, with correction) (0.00, 12.50) (0.00, 100.00) (87.50, 100.00) #> Partial Response (PR) 4 (100.0%) 2 (50.0%) 0 (0.0%) #> 95% CI (Wald, with correction) (87.50, 100.00) (0.00, 100.00) (0.00, 12.50) s_length_proportion(rep(\"CR\", 10), .N_col = 100) #> $n_prop #> [1] 10.0 0.1 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 3.620108 16.379892 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> s_length_proportion(factor(character(0)), .N_col = 100) #> $n_prop #> [1] 0 0 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 0.0 0.5 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> a_length_proportion(rep(\"CR\", 10), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n_prop 10 (10.0%) 0 Responders #> 2 prop_ci (3.62, 16.38) 0 95% CI (Wald, with correction) a_length_proportion(factor(character(0)), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n_prop 0 (0.0%) 0 Responders #> 2 prop_ci (0.00, 0.50) 0 95% CI (Wald, with correction)"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion estimation — estimate_proportion","title":"Proportion estimation — estimate_proportion","text":"analyze function estimate_proportion() creates layout element estimate proportion responders within studied population. primary analysis variable, vars, indicates whether response occurred record. See method parameter options methods use constructing confidence interval proportion. Additionally, stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion estimation — estimate_proportion","text":"","code":"estimate_proportion( lyt, vars, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_proportion( df, .var, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE ) a_proportion( df, .var, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion estimation — estimate_proportion","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights. variables (named list string) list additional analysis variables. long (flag) whether long description required. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_proportion\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (logical data.frame) logical vector used, indicates whether subject responder . TRUE represents successful outcome. data.frame provided, also strata variable names must provided variables list element strata strings. case data.frame, logical vector responses must indicated variable name .var. .var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion estimation — estimate_proportion","text":"estimate_proportion() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion() table layout. s_proportion() returns statistics n_prop (n proportion) prop_ci (proportion CI) given variable. a_proportion() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion estimation — estimate_proportion","text":"estimate_proportion(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_proportion(): Statistics function estimating proportion along confidence interval. a_proportion(): Formatted analysis function used afun estimate_proportion().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion estimation — estimate_proportion","text":"","code":"dta_test <- data.frame( USUBJID = paste0(\"S\", 1:12), ARM = rep(LETTERS[1:3], each = 4), AVAL = rep(LETTERS[1:3], each = 4) ) basic_table() %>% split_cols_by(\"ARM\") %>% estimate_proportion(vars = \"AVAL\") %>% build_table(df = dta_test) #> A B C #> —————————————————————————————————————————————————————————————————— #> Responders #> 95% CI (Wald, with correction) # Case with only logical vector. rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0) s_proportion(rsp_v) #> $n_prop #> [1] 4.0 0.5 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 9.102404 90.897596 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> # Example for Stratified Wilson CI nex <- 100 # Number of example rows dta <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE), \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE), \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE), stringsAsFactors = TRUE ) s_proportion( df = dta, .var = \"rsp\", variables = list(strata = c(\"f1\", \"f2\")), conf_level = 0.90, method = \"strat_wilson\" ) #> $n_prop #> [1] 49.00 0.49 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> lower upper #> 40.80675 56.65017 #> attr(,\"label\") #> [1] \"90% CI (Stratified Wilson, without correction)\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated CDISC data for examples — ex_data","title":"Simulated CDISC data for examples — ex_data","text":"Simulated CDISC data examples","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated CDISC data for examples — ex_data","text":"","code":"tern_ex_adsl tern_ex_adae tern_ex_adlb tern_ex_adpp tern_ex_adrs tern_ex_adtte"},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated CDISC data for examples — ex_data","text":"rds (data.frame) object class tbl_df (inherits tbl, data.frame) 200 rows 21 columns. object class tbl_df (inherits tbl, data.frame) 541 rows 42 columns. object class tbl_df (inherits tbl, data.frame) 4200 rows 50 columns. object class tbl_df (inherits tbl, data.frame) 522 rows 25 columns. object class tbl_df (inherits tbl, data.frame) 1600 rows 29 columns. object class tbl_df (inherits tbl, data.frame) 1000 rows 28 columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Simulated CDISC data for examples — ex_data","text":"tern_ex_adsl: ADSL data tern_ex_adae: ADAE data tern_ex_adlb: ADLB data tern_ex_adpp: ADPP data tern_ex_adrs: ADRS data tern_ex_adtte: ADTTE data","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing data — explicit_na","title":"Missing data — explicit_na","text":"Substitute missing data string factor level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing data — explicit_na","text":"","code":"explicit_na(x, label = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing data — explicit_na","text":"x (factor character) values missing values substituted. label (string) string missing data replaced .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing data — explicit_na","text":"x NA values substituted label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing data — explicit_na","text":"","code":"explicit_na(c(NA, \"a\", \"b\")) #> [1] \"\" \"a\" \"b\" is.na(explicit_na(c(NA, \"a\", \"b\"))) #> [1] FALSE FALSE FALSE explicit_na(factor(c(NA, \"a\", \"b\"))) #> [1] a b #> Levels: a b is.na(explicit_na(factor(c(NA, \"a\", \"b\")))) #> [1] FALSE FALSE FALSE explicit_na(sas_na(c(\"a\", \"\"))) #> [1] \"a\" \"\""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract elements by name — extract_by_name","title":"Extract elements by name — extract_by_name","text":"utility function extracts elements vector x names. Differences standard [ function :","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract elements by name — extract_by_name","text":"","code":"extract_by_name(x, names)"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract elements by name — extract_by_name","text":"x (named vector) extract named elements . names (character) vector names extract.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract elements by name — extract_by_name","text":"NULL x NULL, otherwise extracted elements x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract elements by name — extract_by_name","text":"x NULL, still always NULL returned (base function). x NULL, intersection names made names elements returned. , names appear x returned NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"Prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates, subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"","code":"extract_rsp_biomarkers( variables, data, groups_lists = list(), control = control_logistic(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (named list) controls response definition confidence level produced control_logistic(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_rsp, prop, , lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"can also specify continuous variable rsp use response_definition control convert internally logical variable reflecting binary response.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in logistic regression models with one covariate `RACE`. The subgroups # are defined by the levels of `BMRKR2`. df <- extract_rsp_biomarkers( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\" ), data = adrs_f ) df #> biomarker biomarker_label n_tot n_rsp prop or #> 1 BMRKR1 Continuous Level Biomarker 1 200 164 0.8200000 0.9755036 #> 2 AGE Age 200 164 0.8200000 0.9952416 #> 3 BMRKR1 Continuous Level Biomarker 1 70 53 0.7571429 1.1524547 #> 4 AGE Age 70 53 0.7571429 0.9261012 #> 5 BMRKR1 Continuous Level Biomarker 1 68 58 0.8529412 0.8773122 #> 6 AGE Age 68 58 0.8529412 0.9867104 #> 7 BMRKR1 Continuous Level Biomarker 1 62 53 0.8548387 0.8792921 #> 8 AGE Age 62 53 0.8548387 1.0630262 #> lcl ucl conf_level pval pval_label subgroup var #> 1 0.8804862 1.080775 0.95 0.6352602 p-value (Wald) All Patients ALL #> 2 0.9462617 1.046757 0.95 0.8530389 p-value (Wald) All Patients ALL #> 3 0.9462127 1.403650 0.95 0.1584187 p-value (Wald) LOW BMRKR2 #> 4 0.8487519 1.010500 0.95 0.0844837 p-value (Wald) LOW BMRKR2 #> 5 0.7277189 1.057657 0.95 0.1699778 p-value (Wald) MEDIUM BMRKR2 #> 6 0.8798911 1.106498 0.95 0.8189816 p-value (Wald) MEDIUM BMRKR2 #> 7 0.7189748 1.075357 0.95 0.2103709 p-value (Wald) HIGH BMRKR2 #> 8 0.9595973 1.177603 0.95 0.2418840 p-value (Wald) HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis # Here we group the levels of `BMRKR2` manually, and we add a stratification # variable `STRATA1`. We also here use a continuous variable `EOSDY` # which is then binarized internally (response is defined as this variable # being larger than 750). df_grouped <- extract_rsp_biomarkers( variables = list( rsp = \"EOSDY\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\", strata = \"STRATA1\" ), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ), control = control_logistic( response_definition = \"I(response > 750)\" ) ) df_grouped #> biomarker biomarker_label n_tot n_rsp prop or lcl ucl conf_level #> 1 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95 #> 2 AGE Age 200 0 0 NA NA NA 0.95 #> 3 BMRKR1 Continuous Level Biomarker 1 70 0 0 NA NA NA 0.95 #> 4 AGE Age 70 0 0 NA NA NA 0.95 #> 5 BMRKR1 Continuous Level Biomarker 1 138 0 0 NA NA NA 0.95 #> 6 AGE Age 138 0 0 NA NA NA 0.95 #> 7 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95 #> 8 AGE Age 200 0 0 NA NA NA 0.95 #> pval pval_label subgroup var var_label #> 1 NA p-value (Wald) All Patients ALL All Patients #> 2 NA p-value (Wald) All Patients ALL All Patients #> 3 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2 #> 4 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2 #> 5 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2 #> 7 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> 8 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> row_type #> 1 content #> 2 content #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #> 7 analysis #> 8 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"Prepares response rates odds ratios population subgroups data frames. Simple wrapper h_odds_ratio_subgroups_df() h_proportion_subgroups_df(). Result list two data.frames: prop . variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"","code":"extract_rsp_subgroups( variables, data, groups_lists = list(), conf_level = 0.95, method = NULL, label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"named list two elements: prop: data.frame containing columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. : data.frame containing columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables), optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"","code":"extract_survival_biomarkers( variables, data, groups_lists = list(), control = control_coxreg(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) list parameters returned helper function control_coxreg(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_tot_events, median, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"Prepares estimates median survival times treatment hazard ratios population subgroups data frames. Simple wrapper h_survtime_subgroups_df() h_coxph_subgroups_df(). Result list two data.frames: survtime hr. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"","code":"extract_survival_subgroups( variables, data, groups_lists = list(), control = control_coxph(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing null hypothesis hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval HR. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"named list two elements: survtime: data.frame containing columns arm, n, n_events, median, subgroup, var, var_label, row_type. hr: data.frame containing columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Format extreme values — extreme_format","title":"Format extreme values — extreme_format","text":"rtables formatting functions handle extreme values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format extreme values — extreme_format","text":"","code":"h_get_format_threshold(digits = 2L) h_format_threshold(x, digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format extreme values — extreme_format","text":"digits (integer(1)) number decimal places display. x (numeric(1)) value format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format extreme values — extreme_format","text":"h_get_format_threshold() returns list 2 elements: threshold, low high thresholds, format_string, thresholds formatted strings. h_format_threshold() returns given value, value within digit threshold relation given value digit threshold, formatted string.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Format extreme values — extreme_format","text":"input, apply format specified number digits. value threshold, returns \"<0.01\" e.g. number digits 2. value threshold, returns \">999.99\" e.g. number digits 2. zero, returns \"0.00\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Format extreme values — extreme_format","text":"h_get_format_threshold(): Internal helper function calculate threshold create formatted strings used Formatting Functions. Returns list elements threshold format_string. h_format_threshold(): Internal helper function apply threshold format value. Creates formatted string used Formatting Functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format extreme values — extreme_format","text":"","code":"h_get_format_threshold(2L) #> $threshold #> low high #> 0.01 999.99 #> #> $format_string #> low high #> \"<0.01\" \">999.99\" #> h_format_threshold(0.001) #> [1] \"<0.01\" h_format_threshold(1000) #> [1] \">999.99\""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for confidence interval — f_conf_level","text":"","code":"f_conf_level(conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for confidence interval — f_conf_level","text":"conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for confidence interval — f_conf_level","text":"string.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for p-value — f_pval","text":"","code":"f_pval(test_mean)"},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for p-value — f_pval","text":"test_mean (numeric(1)) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for p-value — f_pval","text":"string.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels and keep only those new group levels — fct_collapse_only","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"collapses levels keeps new group levels, order provided. returned factor levels order given, possible missing level last (included missing values).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"","code":"fct_collapse_only(.f, ..., .na_level = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":".f (factor character) original vector. ... (named character) levels vector provided collapsed new level given respective name. .na_level (string) level use levels, missing new factor. Note level must contained new levels specified ....","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"modified factor collapsed levels. Values levels included given character vector input set missing level .na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"existing NAs input vector replaced missing level. needed, explicit_na() can called separately result.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"","code":"fct_collapse_only(factor(c(\"a\", \"b\", \"c\", \"d\")), TRT = \"b\", CTRL = c(\"c\", \"d\")) #> [1] TRT CTRL CTRL #> Levels: TRT CTRL "},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard specified levels of a factor — fct_discard","title":"Discard specified levels of a factor — fct_discard","text":"discards observations well levels specified factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard specified levels of a factor — fct_discard","text":"","code":"fct_discard(x, discard)"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard specified levels of a factor — fct_discard","text":"x (factor) original factor. discard (character) levels discard.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard specified levels of a factor — fct_discard","text":"modified factor observations well levels discard dropped.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discard specified levels of a factor — fct_discard","text":"","code":"fct_discard(factor(c(\"a\", \"b\", \"c\")), \"c\") #> [1] a b #> Levels: a b"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":null,"dir":"Reference","previous_headings":"","what":"Insertion of explicit missing values in a factor — fct_explicit_na_if","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"inserts explicit missing values factor based condition. Additionally, existing NA values explicitly converted given na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(x, condition, na_level = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"x (factor) original factor. condition (logical) positions insert missing values. na_level (string) level use missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"modified factor inserted existing NA converted na_level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(factor(c(\"a\", \"b\", NA)), c(TRUE, FALSE, FALSE)) #> [1] b #> Levels: a b "},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting functions for Cox proportional hazards regression — fit_coxreg","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"Fitting functions univariate multivariate Cox regression models.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"","code":"fit_coxreg_univar(variables, data, at = list(), control = control_coxreg()) fit_coxreg_multivar(variables, data, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"variables (named list) names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. data (data.frame) dataset containing variables fit models. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"fit_coxreg_univar() returns coxreg.univar class object named list 5 elements: mod: Cox regression models fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model. : Value covariate effect estimated. fit_coxreg_multivar() returns coxreg.multivar class object named list 4 elements: mod: Cox regression model fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"fit_coxreg_univar(): Fit series univariate Cox regression models given inputs. fit_coxreg_multivar(): Fit multivariate Cox regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"using fit_coxreg_univar two study arms.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"","code":"library(survival) set.seed(1, kind = \"Mersenne-Twister\") # Testing dataset [survival::bladder]. dta_bladder <- with( data = bladder[bladder$enum < 5, ], data.frame( time = stop, status = event, armcd = as.factor(rx), covar1 = as.factor(enum), covar2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) plot( survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder), lty = 2:4, xlab = \"Months\", col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") ) # fit_coxreg_univar ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = \"covar1\" ), data = dta_bladder, control = control_coxreg(conf_level = 0.91) ) ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder, control = control_coxreg(conf_level = 0.91, interaction = TRUE) ) ## Cox regression: arm + 1 covariate, stratified analysis. mod3 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", strata = \"covar2\", covariates = c(\"covar1\") ), data = dta_bladder, control = control_coxreg(conf_level = 0.91) ) ## Cox regression: no arm, only covariates. mod4 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder ) # fit_coxreg_multivar ## Cox regression: multivariate Cox regression. multivar_model <- fit_coxreg_multivar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder ) # Example without treatment arm. multivar_covs_model <- fit_coxreg_multivar( variables = list( time = \"time\", event = \"status\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit for logistic regression — fit_logistic","title":"Fit for logistic regression — fit_logistic","text":"Fit (conditional) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit for logistic regression — fit_logistic","text":"","code":"fit_logistic( data, variables = list(response = \"Response\", arm = \"ARMCD\", covariates = NULL, interaction = NULL, strata = NULL), response_definition = \"response\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit for logistic regression — fit_logistic","text":"data (data.frame) data frame model fit. variables (named list string) list additional analysis variables. response_definition (string) definition event terms response. used fitting (conditional) logistic regression model left hand side formula.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit for logistic regression — fit_logistic","text":"fitted logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"model-specification","dir":"Reference","previous_headings":"","what":"Model Specification","title":"Fit for logistic regression — fit_logistic","text":"variables list needs include following elements: arm: Treatment arm variable name. response: response arm variable name. Usually 0/1 variable. covariates: either NULL (covariates) character vector covariate variable names. interaction: either NULL (interaction) string single covariate variable name already included covariates. interaction treatment arm included model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit for logistic regression — fit_logistic","text":"","code":"library(dplyr) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>% mutate( Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), RACE = factor(RACE), SEX = factor(SEX) ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\") ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\"), interaction = \"AGE\" ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"fits Subgroup Treatment Effect Pattern logistic regression models binary (response) outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated odds ratios comparison second level vs. first one. (conditional) logistic regression model fit : response ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"","code":"fit_rsp_step(variables, data, control = c(control_step(), control_logistic()))"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"variables (named list character) list analysis variables: needs response, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_logistic().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"","code":"# Testing dataset with just two treatment arms. library(survival) library(dplyr) adrs_f <- tern_ex_adrs %>% filter( PARAMCD == \"BESRSPI\", ARM %in% c(\"B: Placebo\", \"A: Drug X\") ) %>% mutate( # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations. ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")), RSP = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), SEX = factor(SEX) ) variables <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = \"AGE\", response = \"RSP\" ) # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. # We use a large enough bandwidth to avoid too small subgroups and linear separation in those. step_matrix <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = 0.9)) ) dim(step_matrix) #> [1] 39 11 head(step_matrix) #> Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,] 0.025 0 0.925 1.472755 #> [2,] 0.050 0 0.950 2.028546 #> [3,] 0.075 0 0.975 2.204086 #> [4,] 0.100 0 1.000 2.799776 #> [5,] 0.125 0 1.000 2.969998 #> [6,] 0.150 0 1.000 3.149068 #> Interval Lower Interval Upper n logor se ci_lower ci_upper #> [1,] 0.4459546 11.74529 131 1.835008 1.134560 -0.3886885 4.058703 #> [2,] 0.4459546 12.73387 134 1.895158 1.136248 -0.3318481 4.122163 #> [3,] 0.4459546 14.75099 138 1.947699 1.136969 -0.2807193 4.176116 #> [4,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 #> [5,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 #> [6,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different logistic regression options, including confidence level. step_matrix2 <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = NULL, degree = 1)) ) # Use a global constant model. This is helpful as a reference for the subgroup models. step_matrix3 <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L)) ) # It is also possible to use strata, i.e. use conditional logistic regression models. variables2 <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = \"AGE\", response = \"RSP\", strata = c(\"STRATA1\", \"STRATA2\") ) step_matrix4 <- fit_rsp_step( variables = variables2, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = NULL)) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"fits subgroup treatment effect pattern (STEP) models survival outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated hazard ratios comparison second level vs. first one. model fit : Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"","code":"fit_survival_step( variables, data, control = c(control_step(), control_coxph()) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"variables (named list character) list analysis variables: needs time, event, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_coxph().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"","code":"# Testing dataset with just two treatment arms. library(dplyr) adtte_f <- tern_ex_adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")), is_event = CNSR == 0 ) labels <- c(\"ARM\" = \"Treatment Arm\", \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels variables <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = c(\"AGE\", \"BMRKR2\"), event = \"is_event\", time = \"AVAL\" ) # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. step_matrix <- fit_survival_step( variables = variables, data = adtte_f ) dim(step_matrix) #> [1] 39 12 head(step_matrix) #> Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,] 0.025 0 0.275 1.472755 #> [2,] 0.050 0 0.300 2.028546 #> [3,] 0.075 0 0.325 2.204086 #> [4,] 0.100 0 0.350 2.799776 #> [5,] 0.125 0 0.375 2.969998 #> [6,] 0.150 0 0.400 3.149068 #> Interval Lower Interval Upper n events loghr se ci_lower #> [1,] 0.4459546 4.182444 39 29 0.03729601 0.4243013 -0.7943192 #> [2,] 0.4459546 4.349471 43 31 0.23246622 0.3881347 -0.5282639 #> [3,] 0.4459546 4.626913 46 32 0.07952492 0.3813113 -0.6678314 #> [4,] 0.4459546 4.805767 50 35 0.09020455 0.3638368 -0.6229026 #> [5,] 0.4459546 4.929816 53 37 0.15522702 0.3477999 -0.5264483 #> [6,] 0.4459546 5.020539 57 41 0.11135760 0.3332843 -0.5418676 #> ci_upper #> [1,] 0.8689112 #> [2,] 0.9931963 #> [3,] 0.8268813 #> [4,] 0.8033117 #> [5,] 0.8369023 #> [6,] 0.7645828 # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different Cox regression options. step_matrix2 <- fit_survival_step( variables = variables, data = adtte_f, control = c(control_coxph(conf_level = 0.9), control_step(degree = 2)) ) # Use a global model with cubic interaction and only 5 points. step_matrix3 <- fit_survival_step( variables = variables, data = adtte_f, control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L)) )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a viewport tree for the forest plot — forest_viewport","text":"","code":"forest_viewport( tbl, width_row_names = NULL, width_columns = NULL, width_forest = grid::unit(1, \"null\"), gap_column = grid::unit(1, \"lines\"), gap_header = grid::unit(1, \"lines\"), mat_form = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a viewport tree for the forest plot — forest_viewport","text":"tbl (VTableTree)rtables table object. width_row_names (grid::unit) width row names. width_columns (grid::unit) width column spans. width_forest (grid::unit) width forest plot. gap_column (grid::unit) gap width columns. gap_header (grid::unit) gap width header. mat_form (MatrixPrintForm) matrix print form table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a viewport tree for the forest plot — forest_viewport","text":"viewport tree.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a viewport tree for the forest plot — forest_viewport","text":"","code":"library(grid) tbl <- rtable( header = rheader( rrow(\"\", \"E\", rcell(\"CI\", colspan = 2)), rrow(\"\", \"A\", \"B\", \"C\") ), rrow(\"row 1\", 1, 0.8, 1.1), rrow(\"row 2\", 1.4, 0.8, 1.6), rrow(\"row 3\", 1.2, 0.8, 1.2) ) # \\donttest{ v <- forest_viewport(tbl) #> Warning: `forest_viewport()` was deprecated in tern 0.9.4. #> ℹ `g_forest` now generates `ggplot` objects. This function is no longer used #> within `tern`. #> Warning: `vp_forest_table_part()` was deprecated in tern 0.9.4. #> ℹ `g_forest` now generates `ggplot` objects. This function is no longer used #> within `tern`. #> ℹ The deprecated feature was likely used in the tern package. #> Please report the issue at #> . grid::grid.newpage() showViewport(v) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Format automatically using data significant digits — format_auto","title":"Format automatically using data significant digits — format_auto","text":"Formatting function majority default methods used analyze_vars(). non-derived values, significant digits data used (e.g. range), derived values one digits (measure location dispersion like mean, standard deviation). function can called internally \"auto\" like, example, .formats = c(\"mean\" = \"auto\"). See details see works inner function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format automatically using data significant digits — format_auto","text":"","code":"format_auto(dt_var, x_stat)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format automatically using data significant digits — format_auto","text":"dt_var (numeric) variable data statistics calculated . Used find significant digits. analyze_vars comes .df_row (see rtables::additional_fun_params), row data row splits. column split considered. x_stat (string) string indicating current statistical method used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format automatically using data significant digits — format_auto","text":"string rtables prints table cell.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Format automatically using data significant digits — format_auto","text":"internal function needed work rtables default structure format functions, .e. function(x, ...), x results statistical evaluation. can one element (e.g. .stats = \"mean_sd\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format automatically using data significant digits — format_auto","text":"","code":"x_todo <- c(0.001, 0.2, 0.0011000, 3, 4) res <- c(mean(x_todo[1:3]), sd(x_todo[1:3])) # x is the result coming into the formatting function -> res!! format_auto(dt_var = x_todo, x_stat = \"mean_sd\")(x = res) #> [1] \"0.06737 (0.11486)\" format_auto(x_todo, \"range\")(x = range(x_todo)) #> [1] \"0.0010 - 4.0000\" no_sc_x <- c(0.0000001, 1) format_auto(no_sc_x, \"range\")(x = no_sc_x) #> [1] \"0.0000001 - 1.0000000\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and fraction — format_count_fraction","title":"Format count and fraction — format_count_fraction","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and fraction — format_count_fraction","text":"","code":"format_count_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and fraction — format_count_fraction","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and fraction — format_count_fraction","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and fraction — format_count_fraction","text":"","code":"format_count_fraction(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.5)) #> [1] \"2 (50.0%)\" format_count_fraction_fixed_dp(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"Formats count together fraction special consideration count less 10.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"string format count (fraction %). count less 10, count printed.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x = c(275, 0.9673)) #> [1] \"275 (96.7%)\" format_count_fraction_lt10(x = c(2, 0.6667)) #> [1] \"2\" format_count_fraction_lt10(x = c(9, 1)) #> [1] \"9\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a single extreme value — format_extreme_values","title":"Format a single extreme value — format_extreme_values","text":"Create formatting function single extreme value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a single extreme value — format_extreme_values","text":"","code":"format_extreme_values(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a single extreme value — format_extreme_values","text":"digits (integer(1)) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format a single extreme value — format_extreme_values","text":"rtables formatting function uses threshold digits return formatted extreme value.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a single extreme value — format_extreme_values","text":"","code":"format_fun <- format_extreme_values(2L) format_fun(x = 0.127) #> [1] \"0.13\" format_fun(x = Inf) #> [1] \">999.99\" format_fun(x = 0) #> [1] \"0.00\" format_fun(x = 0.009) #> [1] \"<0.01\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Format extreme values part of a confidence interval — format_extreme_values_ci","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"Formatting Function extreme values part confidence interval. Values formatted e.g. \"(xx.xx, xx.xx)\" number digits 2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"","code":"format_extreme_values_ci(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"digits (integer(1)) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"rtables formatting function uses threshold digits return formatted extreme values confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"","code":"format_fun <- format_extreme_values_ci(2L) format_fun(x = c(0.127, Inf)) #> [1] \"(0.13, >999.99)\" format_fun(x = c(0, 0.009)) #> [1] \"(0.00, <0.01)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction and percentage — format_fraction","title":"Format fraction and percentage — format_fraction","text":"Formats fraction together ratio percent.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction and percentage — format_fraction","text":"","code":"format_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction and percentage — format_fraction","text":"x (named integer) vector elements num denom. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction and percentage — format_fraction","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction and percentage — format_fraction","text":"","code":"format_fraction(x = c(num = 2L, denom = 3L)) #> [1] \"2/3 (66.7%)\" format_fraction(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"Formats fraction together ratio percent fixed single decimal place. Includes trailing zero case whole number percentages always keep one decimal place.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"x (named integer) vector elements num denom. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 4L)) #> [1] \"1/4 (25.0%)\" format_fraction_fixed_dp(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction with lower threshold — format_fraction_threshold","title":"Format fraction with lower threshold — format_fraction_threshold","text":"Formats fraction second element input x fraction. applies lower threshold, just stated fraction smaller .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction with lower threshold — format_fraction_threshold","text":"","code":"format_fraction_threshold(threshold)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction with lower threshold — format_fraction_threshold","text":"threshold (proportion) lower threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction with lower threshold — format_fraction_threshold","text":"rtables formatting function takes numeric input x second element fraction formatted. fraction equal threshold, displayed percentage. positive threshold, returns, e.g. \"<1\" threshold 0.01. zero, just \"0\" returned.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction with lower threshold — format_fraction_threshold","text":"","code":"format_fun <- format_fraction_threshold(0.05) format_fun(x = c(20, 0.1)) #> [1] 10 format_fun(x = c(2, 0.01)) #> [1] \"<5\" format_fun(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":null,"dir":"Reference","previous_headings":"","what":"Format numeric values by significant figures — format_sigfig","title":"Format numeric values by significant figures — format_sigfig","text":"Format numeric values print specified number significant figures.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format numeric values by significant figures — format_sigfig","text":"","code":"format_sigfig(sigfig, format = \"xx\", num_fmt = \"fg\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format numeric values by significant figures — format_sigfig","text":"sigfig (integer(1)) number significant figures display. format (string) format label (string) apply printing value. Decimal places string ignored favor formatting significant figures. Formats options : \"xx\", \"xx / xx\", \"(xx, xx)\", \"xx - xx\", \"xx (xx)\". num_fmt (string) numeric format modifiers apply value. Defaults \"fg\" standard significant figures formatting - fixed (non-scientific notation) format (\"f\") sigfig equal number significant figures instead decimal places (\"g\"). See formatC() format argument options.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format numeric values by significant figures — format_sigfig","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format numeric values by significant figures — format_sigfig","text":"","code":"fmt_3sf <- format_sigfig(3) fmt_3sf(1.658) #> [1] \"1.66\" fmt_3sf(1e1) #> [1] \"10.0\" fmt_5sf <- format_sigfig(5) fmt_5sf(0.57) #> [1] \"0.57000\" fmt_5sf(0.000025645) #> [1] \"0.000025645\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":null,"dir":"Reference","previous_headings":"","what":"Format XX as a formatting function — format_xx","title":"Format XX as a formatting function — format_xx","text":"Translate string x dots interpreted number place holders, others formatting elements.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format XX as a formatting function — format_xx","text":"","code":"format_xx(str)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format XX as a formatting function — format_xx","text":"str (string) template.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format XX as a formatting function — format_xx","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format XX as a formatting function — format_xx","text":"","code":"test <- list(c(1.658, 0.5761), c(1e1, 785.6)) z <- format_xx(\"xx (xx.x)\") sapply(test, z) #> [1] \"2 (0.6)\" \"10 (785.6)\" z <- format_xx(\"xx.x - xx.x\") sapply(test, z) #> [1] \"1.7 - 0.6\" \"10 - 785.6\" z <- format_xx(\"xx.x, incl. xx.x% NE\") sapply(test, z) #> [1] \"1.7, incl. 0.6% NE\" \"10, incl. 785.6% NE\""},{"path":"https://insightsengineering.github.io/tern/main/reference/formatting_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting functions — formatting_functions","title":"Formatting functions — formatting_functions","text":"See list formatting functions created tern work rtables. available formats can listed via formatters::list_valid_format_labels(). Additional custom formats can created via formatters::sprintf_format() function.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a forest plot from an rtable — g_forest","text":"","code":"g_forest( tbl, col_x = attr(tbl, \"col_x\"), col_ci = attr(tbl, \"col_ci\"), vline = 1, forest_header = attr(tbl, \"forest_header\"), xlim = c(0.1, 10), logx = TRUE, x_at = c(0.1, 1, 10), width_row_names = lifecycle::deprecated(), width_columns = NULL, width_forest = lifecycle::deprecated(), lbl_col_padding = 0, rel_width_forest = 0.25, font_size = 12, col_symbol_size = attr(tbl, \"col_symbol_size\"), col = getOption(\"ggplot2.discrete.colour\")[1], ggtheme = NULL, as_list = FALSE, gp = lifecycle::deprecated(), draw = lifecycle::deprecated(), newpage = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a forest plot from an rtable — g_forest","text":"tbl (VTableTree)rtables table least one column single value one column 2 values. col_x (integer(1) NULL) column index estimator. default tries get tbl attribute col_x, otherwise needs manually specified. NULL, points excluded forest plot. col_ci (integer(1) NULL) column index confidence intervals. default tries get tbl attribute col_ci, otherwise needs manually specified. NULL, lines excluded forest plot. vline (numeric(1) NULL) x coordinate vertical line, NULL line omitted. forest_header (character(2)) text displayed left right vline, respectively. vline = NULL forest_header printed. default tries get tbl attribute forest_header. NULL, defaults extracted table possible, set \"Comparison\\nBetter\" \"Treatment\\nBetter\" . xlim (numeric(2)) limits x axis. logx (flag) show x-values logarithm scale. x_at (numeric) x-tick locations, NULL, x_at set vline xlim values. width_row_names Please use lbl_col_padding argument instead. width_columns (numeric) vector column widths. element's position colwidths corresponds column tbl position. NULL, column widths calculated according maximum number characters per column. width_forest Please use rel_width_forest argument instead. lbl_col_padding (numeric) additional padding use calculating spacing first (label) column second column tbl. colwidths specified, width first column becomes colwidths[1] + lbl_col_padding. Defaults 0. rel_width_forest (proportion) proportion total width allocate forest plot. Relative width table 1 - rel_width_forest. as_list = TRUE, parameter ignored. font_size (numeric(1)) font size. col_symbol_size (numeric NULL) column index tbl containing data used determine relative size estimator plot symbol. Typically, symbol size proportional sample size used calculate estimator. NULL, symbol size used subgroups. default tries get tbl attribute col_symbol_size, otherwise needs manually specified. col (character) color(s). ggtheme (theme) graphical theme provided ggplot2 control styling plot. as_list (flag) whether two ggplot objects returned list. TRUE, named list two elements, table plot, returned. FALSE (default) table forest plot printed side--side via cowplot::plot_grid(). gp g_forest now generated ggplot object. argument longer used. draw g_forest now generated ggplot object. argument longer used. newpage g_forest now generated ggplot object. argument longer used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a forest plot from an rtable — g_forest","text":"ggplot forest plot table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a forest plot from an rtable — g_forest","text":"Given rtables::rtable() object least one column single value one column 2 values, converts table ggplot2::ggplot() object generates accompanying forest plot. table forest plot printed side--side.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a forest plot from an rtable — g_forest","text":"","code":"library(dplyr) library(forcats) library(nestcolor) adrs <- tern_ex_adrs n_records <- 20 adrs_labels <- formatters::var_labels(adrs, fill = TRUE) adrs <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% slice(seq_len(n_records)) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs) <- c(adrs_labels, \"Response\") df <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA2\")), data = adrs ) # Full commonly used response table. tbl <- basic_table() %>% tabulate_rsp_subgroups(df) g_forest(tbl) # Odds ratio only table. tbl_or <- basic_table() %>% tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\")) g_forest( tbl_or, forest_header = c(\"Comparison\\nBetter\", \"Treatment\\nBetter\") ) # Survival forest plot example. adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = TRUE) adtte_f <- adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\"), SEX %in% c(\"M\", \"F\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")), SEX = droplevels(SEX), AVALU = as.character(AVALU), is_event = CNSR == 0 ) labels <- list( \"ARM\" = adtte_labels[\"ARM\"], \"SEX\" = adtte_labels[\"SEX\"], \"AVALU\" = adtte_labels[\"AVALU\"], \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels) df <- extract_survival_subgroups( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) table_hr <- basic_table() %>% tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) g_forest(table_hr) # Works with any `rtable`. tbl <- rtable( header = c(\"E\", \"CI\", \"N\"), rrow(\"\", 1, c(.8, 1.2), 200), rrow(\"\", 1.2, c(1.1, 1.4), 50) ) g_forest( tbl = tbl, col_x = 1, col_ci = 2, xlim = c(0.5, 2), x_at = c(0.5, 1, 2), col_symbol_size = 3 ) tbl <- rtable( header = rheader( rrow(\"\", rcell(\"A\", colspan = 2)), rrow(\"\", \"c1\", \"c2\") ), rrow(\"row 1\", 1, c(.8, 1.2)), rrow(\"row 2\", 1.2, c(1.1, 1.4)) ) g_forest( tbl = tbl, col_x = 1, col_ci = 2, xlim = c(0.5, 2), x_at = c(0.5, 1, 2), vline = 1, forest_header = c(\"Hello\", \"World\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual patient plots — g_ipp","title":"Individual patient plots — g_ipp","text":"Line plot(s) displaying trend patients' parameter values time rendered. Patients' individual baseline values can added plot(s) reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual patient plots — g_ipp","text":"","code":"g_ipp( df, xvar, yvar, xlab, ylab, id_var = \"USUBJID\", title = \"Individual Patient Plots\", subtitle = \"\", caption = NULL, add_baseline_hline = FALSE, yvar_baseline = \"BASE\", ggtheme = nestcolor::theme_nest(), plotting_choices = c(\"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\"), max_obs_per_plot = 4, col = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual patient plots — g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (string) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. plotting_choices (string) specifies options displaying plots. Must one \"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\". max_obs_per_plot (integer(1)) number observations plotted one plot. Ignored plotting_choices \"separate_by_obs\". col (character) line colors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual patient plots — g_ipp","text":"ggplot object list ggplot objects.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Individual patient plots — g_ipp","text":"g_ipp(): Plotting function individual patient plots , depending user preference, renders single graphic compiles list graphics show trends individual's parameter values time.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual patient plots — g_ipp","text":"","code":"library(dplyr) library(nestcolor) # Select a small sample of data to plot. adlb <- tern_ex_adlb %>% filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>% slice(1:36) plot_list <- g_ipp( df = adlb, xvar = \"AVISIT\", yvar = \"AVAL\", xlab = \"Visit\", ylab = \"SGOT/ALT (U/L)\", title = \"Individual Patient Plots\", add_baseline_hline = TRUE, plotting_choices = \"split_by_max_obs\", max_obs_per_plot = 5 ) plot_list #> [[1]] #> #> [[2]] #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier plot — g_km","title":"Kaplan-Meier plot — g_km","text":"survival model, graphic rendered along tabulated annotation including number patient risk given time median survival per group.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier plot — g_km","text":"","code":"g_km( df, variables, control_surv = control_surv_timepoint(), col = NULL, lty = NULL, lwd = 0.5, censor_show = TRUE, pch = 3, size = 2, max_time = NULL, xticks = NULL, xlab = \"Days\", yval = c(\"Survival\", \"Failure\"), ylab = paste(yval, \"Probability\"), ylim = NULL, title = NULL, footnotes = NULL, font_size = 10, ci_ribbon = FALSE, annot_at_risk = TRUE, annot_at_risk_title = TRUE, annot_surv_med = TRUE, annot_coxph = FALSE, annot_stats = NULL, annot_stats_vlines = FALSE, control_coxph_pw = control_coxph(), ref_group_coxph = NULL, control_annot_surv_med = control_surv_med_annot(), control_annot_coxph = control_coxph_annot(), legend_pos = NULL, rel_height_plot = 0.75, ggtheme = NULL, as_list = FALSE, draw = lifecycle::deprecated(), newpage = lifecycle::deprecated(), gp = lifecycle::deprecated(), vp = lifecycle::deprecated(), name = lifecycle::deprecated(), annot_coxph_ref_lbls = lifecycle::deprecated(), position_coxph = lifecycle::deprecated(), position_surv_med = lifecycle::deprecated(), width_annots = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier plot — g_km","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strata (character NULL) variable names indicating stratification factors. control_surv (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string)\"plain\" (default), \"log\", \"log-log\" confidence interval type, see survival::survfit(). Note option \"none\" longer supported. col (character) lines colors. Length vector equal number strata survival::survfit(). lty (numeric) line type. vector given, length equal number strata survival::survfit(). lwd (numeric) line width. vector given, length equal number strata survival::survfit(). censor_show (flag) whether show censored observations. pch (string) name symbol character use point symbol indicate censored cases. size (numeric(1)) size censored point symbols. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL). xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. xlab (string) x-axis label. yval (string) type plot, plotted y-axis. Options Survival (default) Failure probability. ylab (string) y-axis label. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. title (string) plot title. footnotes (string) plot footnotes. font_size (numeric(1)) font size use text. ci_ribbon (flag) whether confidence interval drawn around Kaplan-Meier curve. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE. annot_surv_med (flag) compute add annotation table Kaplan-Meier curve estimating median survival time per group. annot_coxph (flag) whether add annotation table survival::coxph() model. annot_stats (string NULL) statistics annotations add plot. Options median (median survival follow-time) min (minimum survival follow-time). annot_stats_vlines (flag) add vertical lines corresponding statistics specified annot_stats. annot_stats NULL lines added. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. ref_group_coxph (string NULL) level arm variable use reference group calculations annot_coxph table. NULL (default), uses first level arm variable. control_annot_surv_med (list) parameters control position size annotation table added plot annot_surv_med = TRUE, specified using control_surv_med_annot() function. Parameter options : x, y, w, h, fill. See control_surv_med_annot() details. control_annot_coxph (list) parameters control position size annotation table added plot annot_coxph = TRUE, specified using control_coxph_annot() function. Parameter options : x, y, w, h, fill, ref_lbls. See control_coxph_annot() details. legend_pos (numeric(2) NULL) vector containing x- y-coordinates, respectively, legend position relative KM plot area. NULL (default), legend positioned bottom right corner plot, middle right plot needed prevent overlapping. rel_height_plot (proportion) proportion total figure height allocate Kaplan-Meier plot. Relative height patients risk table 1 - rel_height_plot. annot_at_risk = FALSE as_list = TRUE, parameter ignored. ggtheme (theme) graphical theme provided ggplot2 format Kaplan-Meier plot. as_list (flag) whether two ggplot objects returned list annot_at_risk = TRUE. TRUE, named list two elements, plot table, returned. FALSE (default) patients risk table printed plot via cowplot::plot_grid(). draw function longer generates grob objects. newpage function longer generates grob objects. gp function longer generates grob objects. vp function longer generates grob objects. name function longer generates grob objects. annot_coxph_ref_lbls Please use ref_lbls element control_annot_coxph instead. position_coxph Please use x y elements control_annot_coxph instead. position_surv_med Please use x y elements control_annot_surv_med instead. width_annots Please use w element control_annot_surv_med (surv_med) control_annot_coxph (coxph).\"","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier plot — g_km","text":"ggplot Kaplan-Meier plot (optionally) summary table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier plot — g_km","text":"","code":"library(dplyr) library(nestcolor) df <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) variables <- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\") # Basic examples g_km(df = df, variables = variables) g_km(df = df, variables = variables, yval = \"Failure\") # Examples with customization parameters applied g_km( df = df, variables = variables, control_surv = control_surv_timepoint(conf_level = 0.9), col = c(\"grey25\", \"grey50\", \"grey75\"), annot_at_risk_title = FALSE, lty = 1:3, font_size = 8 ) g_km( df = df, variables = variables, annot_stats = c(\"min\", \"median\"), annot_stats_vlines = TRUE, max_time = 3000, ggtheme = ggplot2::theme_minimal() ) # Example with pairwise Cox-PH analysis annotation table, adjusted annotation tables g_km( df = df, variables = variables, annot_coxph = TRUE, control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99), control_annot_coxph = control_coxph_annot(x = 0.26, w = 0.35), control_annot_surv_med = control_surv_med_annot(x = 0.8, y = 0.9, w = 0.35) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Line plot with optional table — g_lineplot","title":"Line plot with optional table — g_lineplot","text":"Line plot optional table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Line plot with optional table — g_lineplot","text":"","code":"g_lineplot( df, alt_counts_df = NULL, variables = control_lineplot_vars(), mid = \"mean\", interval = \"mean_ci\", whiskers = c(\"mean_ci_lwr\", \"mean_ci_upr\"), table = NULL, sfun = s_summary, ..., mid_type = \"pl\", mid_point_size = 2, position = ggplot2::position_dodge(width = 0.4), legend_title = NULL, legend_position = \"bottom\", ggtheme = nestcolor::theme_nest(), xticks = NULL, xlim = NULL, ylim = NULL, x_lab = obj_label(df[[variables[[\"x\"]]]]), y_lab = NULL, y_lab_add_paramcd = TRUE, y_lab_add_unit = TRUE, title = \"Plot of Mean and 95% Confidence Limits by Visit\", subtitle = \"\", subtitle_add_paramcd = TRUE, subtitle_add_unit = TRUE, caption = NULL, table_format = NULL, table_labels = NULL, table_font_size = 3, errorbar_width = 0.45, newpage = lifecycle::deprecated(), col = NULL, linetype = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Line plot with optional table — g_lineplot","text":"df (data.frame) data set containing analysis variables. alt_counts_df (data.frame NULL) data set used () counts objects groups stratification. variables (named character) vector variable names df include: x (string) name x-axis variable. y (string) name y-axis variable. group_var (string NULL) name grouping variable (strata), .e. treatment arm. Can NA indicate lack groups. subject_var (string NULL) name subject variable. applies group_var NULL. paramcd (string NA) name variable parameter's code. Used y-axis label plot's subtitle. Can NA paramcd added y-axis label subtitle. y_unit (string NA) name variable units y. Used y-axis label plot's subtitle. Can NA y unit added y-axis label subtitle. facet_var (string NA) name secondary grouping variable used plot faceting, .e. treatment arm. Can NA indicate lack groups. mid (character NULL) names statistics plotted midpoints. statistics indicated mid variable must present object returned sfun, double numeric type vector length one. interval (character NULL) names statistics plotted intervals. statistics indicated interval variable must present object returned sfun, double numeric type vector length two. Set interval = NULL intervals added plot. whiskers (character) names interval whiskers plotted. Names must match names list element interval returned sfun (e.g. mean_ci_lwr element sfun(x)[[\"mean_ci\"]]). possible specify one whisker , suppress whiskers setting interval = NULL. table (character NULL) names statistics displayed table plot. statistics indicated table variable must present object returned sfun. sfun (function) function compute values required statistics. must return named list atomic vectors. names list elements refer names statistics used mid, interval, table. must able accept input vector data statistics computed. ... optional arguments sfun. mid_type (string) controls type mid plot, can point (\"p\"), line (\"l\"), point line (\"pl\"). mid_point_size (numeric(1)) font size mid plot points. position (character call) geom element position adjustment, either string, result call position adjustment function. legend_title (string) legend title. legend_position (string) position plot legend (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector). ggtheme (theme) graphical theme provided ggplot2 control styling plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis, use variables$x numeric. NULL (default), labeling::extended() used determine optimal tick positions x-axis. variables$x numeric, argument ignored. xlim (numeric(2)) vector containing lower upper limits x-axis, respectively. NULL (default), default scale range used. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. x_lab (string NULL) x-axis label. NULL label added. y_lab (string NULL) y-axis label. NULL label added. y_lab_add_paramcd (flag) whether paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added y-axis label (y_lab). y_lab_add_unit (flag) whether y-axis unit, .e. unique(df[[variables[\"y_unit\"]]]) added y-axis label (y_lab). title (string) plot title. subtitle (string) plot subtitle. subtitle_add_paramcd (flag) whether paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added plot's subtitle (subtitle). subtitle_add_unit (flag) whether y-axis unit, .e. unique(df[[variables[\"y_unit\"]]]) added plot's subtitle (subtitle). caption (string) optional caption plot. table_format (named character NULL) format patterns descriptive statistics used (optional) table appended plot. passed directly h_format_row function format parameter. Names table_format must match names statistics returned sfun function. table_labels (named character NULL) labels descriptive statistics used (optional) table appended plot. Names table_labels must match names statistics returned sfun function. table_font_size (numeric(1)) font size text table. errorbar_width (numeric(1)) width error bars. newpage used. col (character) color(s). See ?ggplot2::aes_colour_fill_alpha example values. linetype (character) line type(s). See ?ggplot2::aes_linetype_size_shape example values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Line plot with optional table — g_lineplot","text":"ggplot line plot (statistics table applicable).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Line plot with optional table — g_lineplot","text":"","code":"library(nestcolor) adsl <- tern_ex_adsl adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == \"Y\", PARAMCD == \"ALT\", AVISIT != \"SCREENING\") adlb$AVISIT <- droplevels(adlb$AVISIT) adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, no stratification with group_var g_lineplot(adlb, variables = control_lineplot_vars(group_var = NA)) # Mean, upper whisker of CI, no group_var(strata) counts N g_lineplot( adlb, whiskers = \"mean_ci_upr\", title = \"Plot of Mean and Upper 95% Confidence Limit by Visit\" ) # Median with CI g_lineplot( adlb, adsl, mid = \"median\", interval = \"median_ci\", whiskers = c(\"median_ci_lwr\", \"median_ci_upr\"), title = \"Plot of Median and 95% Confidence Limits by Visit\" ) # Mean, +/- SD g_lineplot(adlb, adsl, interval = \"mean_sdi\", whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"), title = \"Plot of Median +/- SD by Visit\" ) # Mean with CI plot with stats table g_lineplot(adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\")) # Mean with CI, table and customized confidence level g_lineplot( adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"), control = control_analyze_vars(conf_level = 0.80), title = \"Plot of Mean and 80% Confidence Limits by Visit\" ) # Mean with CI, table, filtered data adlb_f <- dplyr::filter(adlb, ARMCD != \"ARM A\" | AVISIT == \"BASELINE\") g_lineplot(adlb_f, table = c(\"n\", \"mean\"))"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a STEP graph — g_step","title":"Create a STEP graph — g_step","text":"Based STEP results, creates ggplot graph showing estimated HR along continuous biomarker value subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a STEP graph — g_step","text":"","code":"g_step( df, use_percentile = \"Percentile Center\" %in% names(df), est = list(col = \"blue\", lty = 1), ci_ribbon = list(fill = getOption(\"ggplot2.discrete.colour\")[1], alpha = 0.5), col = getOption(\"ggplot2.discrete.colour\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a STEP graph — g_step","text":"df (tibble) result tidy.step(). use_percentile (flag) whether use percentiles x axis actual biomarker values. est (named list)col lty settings estimate line. ci_ribbon (named list NULL)fill alpha settings confidence interval ribbon area, NULL plot CI ribbon. col (character) color(s).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a STEP graph — g_step","text":"ggplot STEP graph.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a STEP graph — g_step","text":"","code":"library(nestcolor) library(survival) lung$sex <- factor(lung$sex) # Survival example. vars <- list( time = \"time\", event = \"status\", arm = \"sex\", biomarker = \"age\" ) step_matrix <- fit_survival_step( variables = vars, data = lung, control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) step_data <- broom::tidy(step_matrix) # Default plot. g_step(step_data) # Add the reference 1 horizontal line. library(ggplot2) g_step(step_data) + ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2) # Use actual values instead of percentiles, different color for estimate and no CI, # use log scale for y axis. g_step( step_data, use_percentile = FALSE, est = list(col = \"blue\", lty = 1), ci_ribbon = NULL ) + scale_y_log10() # Adding another curve based on additional column. step_data$extra <- exp(step_data$`Percentile Center`) g_step(step_data) + ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = \"green\") # Response example. vars <- list( response = \"status\", arm = \"sex\", biomarker = \"age\" ) step_matrix <- fit_rsp_step( variables = vars, data = lung, control = c( control_logistic(response_definition = \"I(response == 2)\"), control_step() ) ) step_data <- broom::tidy(step_matrix) g_step(step_data)"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":null,"dir":"Reference","previous_headings":"","what":"Horizontal waterfall plot — g_waterfall","title":"Horizontal waterfall plot — g_waterfall","text":"basic waterfall plot visualizes quantity height ordered value markup.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Horizontal waterfall plot — g_waterfall","text":"","code":"g_waterfall( height, id, col_var = NULL, col = getOption(\"ggplot2.discrete.colour\"), xlab = NULL, ylab = NULL, col_legend_title = NULL, title = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Horizontal waterfall plot — g_waterfall","text":"height (numeric) vector containing values plotted waterfall bars. id (character) vector containing identifiers use x-axis label waterfall bars. col_var (factor, character, NULL) categorical variable bar coloring. NULL default. col (character) color(s). xlab (string) x label. Default \"ID\". ylab (string) y label. Default \"Value\". col_legend_title (string) text displayed legend title. title (string) text displayed plot title.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Horizontal waterfall plot — g_waterfall","text":"ggplot waterfall plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Horizontal waterfall plot — g_waterfall","text":"","code":"library(dplyr) library(nestcolor) g_waterfall(height = c(3, 5, -1), id = letters[1:3]) g_waterfall( height = c(3, 5, -1), id = letters[1:3], col_var = letters[1:3] ) adsl_f <- tern_ex_adsl %>% select(USUBJID, STUDYID, ARM, ARMCD, SEX) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"OVRINV\") %>% mutate(pchg = rnorm(n(), 10, 50)) adrs_f <- head(adrs_f, 30) adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ] head(adrs_f) #> # A tibble: 5 × 30 #> STUDYID COUNTRY SITEID SUBJID AGE SEX ARMCD ARM ACTARMCD ACTARM RACE #> #> 1 AB12345 BRA BRA-1 id-105 37.8 F ARM A A: Drug… ARM A A: Dr… ASIAN #> 2 AB12345 BRA BRA-1 id-171 29.8 F ARM B B: Plac… ARM B B: Pl… ASIAN #> 3 AB12345 BRA BRA-1 id-177 38.9 F ARM B B: Plac… ARM B B: Pl… ASIAN #> 4 AB12345 BRA BRA-1 id-23 41.3 F ARM A A: Drug… ARM A A: Dr… AMER… #> 5 AB12345 BRA BRA-1 id-59 26.7 F ARM A A: Drug… ARM A A: Dr… ASIAN #> # ℹ 19 more variables: TRTSDTM , TRTEDTM , EOSDY , #> # STRATA1 , STRATA2 , BMRKR1 , BMRKR2 , REGION1 , #> # SAFFL , USUBJID , AVISIT , PARAMCD , PARAM , #> # AVALC , AVAL , ADTM , AVISITN , DTHFL , #> # pchg g_waterfall( height = adrs_f$pchg, id = adrs_f$USUBJID, col_var = adrs_f$AVALC ) g_waterfall( height = adrs_f$pchg, id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID), col_var = adrs_f$SEX ) g_waterfall( height = adrs_f$pchg, id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID), xlab = \"ID\", ylab = \"Percentage Change\", title = \"Waterfall plot\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to return a named list of covariate names — get_covariates","title":"Utility function to return a named list of covariate names — get_covariates","text":"Utility function return named list covariate names","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to return a named list of covariate names — get_covariates","text":"","code":"get_covariates(covariates)"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to return a named list of covariate names — get_covariates","text":"covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to return a named list of covariate names — get_covariates","text":"named list character vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth function with optional grouping — get_smooths","title":"Smooth function with optional grouping — get_smooths","text":"produces loess smoothed estimates y Student confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth function with optional grouping — get_smooths","text":"","code":"get_smooths(df, x, y, groups = NULL, level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth function with optional grouping — get_smooths","text":"df (data.frame) data set containing analysis variables. x (string) x column name. y (string) y column name. groups (character NULL) vector optional grouping variables names. level (proportion) level confidence interval use (0.95 default).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth function with optional grouping — get_smooths","text":"data.frame original x, smoothed y, ylow, yhigh, optional groups variables formatted factor type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert list of groups to a data frame — groups_list_to_df","title":"Convert list of groups to a data frame — groups_list_to_df","text":"converts list group levels data frame format expected rtables::add_combo_levels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert list of groups to a data frame — groups_list_to_df","text":"","code":"groups_list_to_df(groups_list)"},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert list of groups to a data frame — groups_list_to_df","text":"groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert list of groups to a data frame — groups_list_to_df","text":"tibble required format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert list of groups to a data frame — groups_list_to_df","text":"","code":"grade_groups <- list( \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"), \"Grade 3-4 (%)\" = c(\"3\", \"4\"), \"Grade 5 (%)\" = \"5\" ) groups_list_to_df(grade_groups) #> # A tibble: 3 × 4 #> valname label levelcombo exargs #> #> 1 AnyGrade Any Grade (%) #> 2 Grade34 Grade 3-4 (%) #> 3 Grade5 Grade 5 (%) "},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"Helper function prepare ADLB data frame used input count_abnormal_by_worst_grade(). following pre-processing steps applied: adlb filtered variable avisit include post-baseline visits. adlb filtered variables worst_flag_low worst_flag_high worst grades (either direction) included. standard lab grade variable atoxgr, following two variables derived added adlb: grade direction variable (e.g. GRADE_DIR). variable takes value \"HIGH\" atoxgr > 0, \"LOW\" atoxgr < 0, \"ZERO\" otherwise. toxicity grade variable (e.g. GRADE_ANL) negative values atoxgr replaced absolute values. Unused factor levels dropped adlb via droplevels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"","code":"h_adlb_abnormal_by_worst_grade( adlb, atoxgr = \"ATOXGR\", avisit = \"AVISIT\", worst_flag_low = \"WGRLOFL\", worst_flag_high = \"WGRHIFL\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"adlb (data.frame) ADLB data frame. atoxgr (string) name analysis toxicity grade variable. must factor variable. avisit (string) name analysis visit variable. worst_flag_low (string) name worst low lab grade flag variable. variable set \"Y\" indicating records worst low lab grades. worst_flag_high (string) name worst high lab grade flag variable. variable set \"Y\" indicating records worst high lab grades.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"h_adlb_abnormal_by_worst_grade() returns adlb data frame two new variables: GRADE_DIR GRADE_ANL.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"","code":"h_adlb_abnormal_by_worst_grade(tern_ex_adlb) %>% dplyr::select(ATOXGR, GRADE_DIR, GRADE_ANL) %>% head(10) #> # A tibble: 10 × 3 #> ATOXGR GRADE_DIR GRADE_ANL #> #> 1 -3 LOW 3 #> 2 0 ZERO 0 #> 3 0 ZERO 0 #> 4 2 HIGH 2 #> 5 0 ZERO 0 #> 6 0 ZERO 0 #> 7 -4 LOW 4 #> 8 1 HIGH 1 #> 9 -1 LOW 1 #> 10 0 ZERO 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"Helper function prepare df generate patient count shift table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"","code":"h_adlb_worsen( adlb, worst_flag_low = NULL, worst_flag_high = NULL, direction_var )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"adlb (data.frame) ADLB data frame. worst_flag_low (named vector) worst low post-baseline lab grade flag variable. See implemented following examples. worst_flag_high (named vector) worst high post-baseline lab grade flag variable. See implemented following examples. direction_var (string) name direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"h_adlb_worsen() returns adlb data.frame containing worst labs specified according worst_flag_low worst_flag_high direction specified according direction_var. instance, lab needed low direction , records flagged worst_flag_low selected. lab needed low high directions, worst low records selected low direction, worst high record selected high direction.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"Helper function merges ADSL ADLB datasets missing lab test records inserted output dataset. Remember na_level must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"","code":"h_adsl_adlb_merge_using_worst_flag( adsl, adlb, worst_flag = c(WGRHIFL = \"Y\"), by_visit = FALSE, no_fillin_visits = c(\"SCREENING\", \"BASELINE\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"adsl (data.frame) ADSL data frame. adlb (data.frame) ADLB data frame. worst_flag (named character) worst post-baseline lab flag variable. See implemented following examples. by_visit (flag) defaults FALSE generate worst grade per patient. worst grade per patient per visit specified worst_flag, by_visit TRUE generate worst grade patient per visit. no_fillin_visits (named character) visits considered post-baseline worst toxicity grade. Defaults c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"df containing variables shared adlb adsl along variables PARAM, PARAMCD, ATOXGR, BTOXGR relevant analysis. Optionally, AVISIT AVISITN included by_visit = TRUE no_fillin_visits = c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"result data missing records created following situations: Patients present adsl lab data adlb (baseline post-baseline). Patients post-baseline lab values. Patients without post-baseline values flagged worst.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"","code":"# `h_adsl_adlb_merge_using_worst_flag` adlb_out <- h_adsl_adlb_merge_using_worst_flag( tern_ex_adsl, tern_ex_adlb, worst_flag = c(\"WGRHIFL\" = \"Y\") ) # `h_adsl_adlb_merge_using_worst_flag` by visit example adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag( tern_ex_adsl, tern_ex_adlb, worst_flag = c(\"WGRLOVFL\" = \"Y\"), by_visit = TRUE )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to return results of a linear model — h_ancova","text":"","code":"h_ancova(.var, .df_row, variables, interaction_item = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to return results of a linear model — h_ancova","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". interaction_item (string NULL) name variable interactions arm. interaction needed, default option NULL.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to return results of a linear model — h_ancova","text":"summary linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to return results of a linear model — h_ancova","text":"","code":"h_ancova( .var = \"Sepal.Length\", .df_row = iris, variables = list(arm = \"Species\", covariates = c(\"Petal.Length * Petal.Width\", \"Sepal.Width\")) ) #> Species emmean SE df lower.CL upper.CL #> setosa 6.15 0.3371 143 5.49 6.82 #> versicolor 5.72 0.0668 143 5.59 5.85 #> virginica 5.41 0.1488 143 5.11 5.70 #> #> Confidence level used: 0.95"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Helper function s_count_occurrences_by_grade() insert grade groupings list individual grade frequencies. order final result follows order grade_groups. elements -grade group (), .e. grade group equal refs moved end. Grade groups names must unique.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups( grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"grade_groups (named list character) list containing groupings grades. refs (named list numeric) named list name corresponds reference grade level entry represents count. remove_single (flag)TRUE include elements one-element grade groups output list; case grade groups names included output. only_grade_groups set TRUE argument ignored. only_grade_groups (flag) whether specified grade groups included, individual grade rows removed (TRUE), grades grade groups displayed (FALSE).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Formatted list grade groupings.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups( list( \"Any Grade\" = as.character(1:5), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-4\" = c(\"3\", \"4\") ), list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #> #> $`Grade 1-2` #> [1] 30 #> #> $`1` #> [1] 10 #> #> $`2` #> [1] 20 #> #> $`Grade 3-4` #> [1] 70 #> #> $`3` #> [1] 30 #> #> $`4` #> [1] 40 #> #> $`5` #> [1] 50 #> h_append_grade_groups( list( \"Any Grade\" = as.character(5:1), \"Grade A\" = \"5\", \"Grade B\" = c(\"4\", \"3\") ), list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #> #> $`Grade A` #> [1] 50 #> #> $`Grade B` #> [1] 70 #> #> $`4` #> [1] 40 #> #> $`3` #> [1] 30 #> #> $`2` #> [1] 20 #> #> $`1` #> [1] 10 #> h_append_grade_groups( list( \"Any Grade\" = as.character(1:5), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-4\" = c(\"3\", \"4\") ), list(\"1\" = 10, \"2\" = 5, \"3\" = 0) ) #> $`Any Grade` #> [1] 15 #> #> $`Grade 1-2` #> [1] 15 #> #> $`1` #> [1] 10 #> #> $`2` #> [1] 5 #> #> $`Grade 3-4` #> [1] 0 #> #> $`3` #> [1] 0 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain column indices — h_col_indices","title":"Obtain column indices — h_col_indices","text":"Helper function extract column indices VTableTree given vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain column indices — h_col_indices","text":"","code":"h_col_indices(table_tree, col_names)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain column indices — h_col_indices","text":"table_tree (VTableTree)rtables table object extract indices . col_names (character) vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain column indices — h_col_indices","text":"vector column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_cumulative() — h_count_cumulative","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"Helper function calculate count fraction x values lower upper tail given threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"","code":"h_count_cumulative( x, threshold, lower_tail = TRUE, include_eq = TRUE, na.rm = TRUE, .N_col )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"x (numeric) vector numbers want analyze. threshold (numeric(1)) cutoff value threshold count values x. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"named vector items: count: count values less , less equal , greater , greater equal threshold user specification. fraction: fraction count.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"","code":"set.seed(1, kind = \"Mersenne-Twister\") x <- c(sample(1:10, 10), NA) .N_col <- length(x) h_count_cumulative(x, 5, .N_col = .N_col) #> count fraction #> 5.0000000 0.4545455 h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col) #> count fraction #> 6.0000000 0.5454545 h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col) #> count fraction #> 10.0000000 0.9090909 h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col) #> count fraction #> 0 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for Cox proportional hazards regression — h_cox_regression","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"Helper functions used fit_coxreg_univar() fit_coxreg_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"","code":"h_coxreg_univar_formulas(variables, interaction = FALSE) h_coxreg_multivar_formula(variables) h_coxreg_univar_extract(effect, covar, data, mod, control = control_coxreg()) h_coxreg_multivar_extract(var, data, mod, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"variables (named list string) list additional analysis variables. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE. effect (string) treatment variable. covar (string) name covariate model. data (data.frame) dataset containing variables summarize. mod (coxph) Cox regression model fitted survival::coxph(). control (list) list controls returned control_coxreg(). var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"h_coxreg_univar_formulas() returns character vector coercible formulas (e.g stats::.formula()). h_coxreg_multivar_formula() returns string coercible formula (e.g stats::.formula()). h_coxreg_univar_extract() returns data.frame variables effect, term, term_label, level, n, hr, lcl, ucl, pval. h_coxreg_multivar_extract() returns data.frame variables pval, hr, lcl, ucl, level, n, term, term_label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"h_coxreg_univar_formulas(): Helper Cox regression formula. Creates list formulas. used internally fit_coxreg_univar() comparison univariate Cox regression models. h_coxreg_multivar_formula(): Helper multivariate Cox regression formula. Creates formulas string. used internally fit_coxreg_multivar() comparison multivariate Cox regression models. Interactions included multivariate Cox regression model. h_coxreg_univar_extract(): Utility function help tabulate result univariate Cox regression model. h_coxreg_multivar_extract(): Tabulation multivariate Cox regressions. Utility function help tabulate result multivariate Cox regression model treatment/covariate variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"","code":"# `h_coxreg_univar_formulas` ## Simple formulas. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\") ) ) #> ref #> \"survival::Surv(time, status) ~ armcd\" #> X #> \"survival::Surv(time, status) ~ armcd + X\" #> y #> \"survival::Surv(time, status) ~ armcd + y\" ## Addition of an optional strata. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"), strata = \"SITE\" ) ) #> ref #> \"survival::Surv(time, status) ~ armcd + strata(SITE)\" #> X #> \"survival::Surv(time, status) ~ armcd + X + strata(SITE)\" #> y #> \"survival::Surv(time, status) ~ armcd + y + strata(SITE)\" ## Inclusion of the interaction term. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"), strata = \"SITE\" ), interaction = TRUE ) #> ref #> \"survival::Surv(time, status) ~ armcd + strata(SITE)\" #> X #> \"survival::Surv(time, status) ~ armcd * X + strata(SITE)\" #> y #> \"survival::Surv(time, status) ~ armcd * y + strata(SITE)\" ## Only covariates fitted in separate models. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", covariates = c(\"X\", \"y\") ) ) #> X y #> \"survival::Surv(time, status) ~ 1 + X\" \"survival::Surv(time, status) ~ 1 + y\" # `h_coxreg_multivar_formula` h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\") ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE\" # Addition of an optional strata. h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\"), strata = \"SITE\" ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE + strata(SITE)\" # Example without treatment arm. h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", covariates = c(\"RACE\", \"AGE\"), strata = \"SITE\" ) ) #> [1] \"survival::Surv(AVAL, event) ~ 1 + RACE + AGE + strata(SITE)\" library(survival) dta_simple <- data.frame( time = c(5, 5, 10, 10, 5, 5, 10, 10), status = c(0, 0, 1, 0, 0, 1, 1, 1), armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c(\"A\", \"B\")), var1 = c(45, 55, 65, 75, 55, 65, 85, 75), var2 = c(\"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"U\") ) mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_univar_extract( effect = \"armcd\", covar = \"armcd\", mod = mod, data = dta_simple ) result #> effect term term_label level n hr lcl ucl #> 1 Treatment: armcd B vs control (A) B 8 6.551448 0.4606904 93.16769 #> pval #> 1 0.165209 mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_multivar_extract( var = \"var1\", mod = mod, data = dta_simple ) result #> pval hr lcl ucl level n term term_label #> 2 0.4456195 0.9423284 0.808931 1.097724 var1 8 var1 var1"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to tidy survival fit data — h_data_plot","title":"Helper function to tidy survival fit data — h_data_plot","text":"Convert survival fit data data frame designed plotting within g_km. starts broom::tidy() result, : Post-processes strata column factor. Extends stratum additional first row time 0 probability 1 downstream plot lines start coordinates. Adds censor column. Filters rows max_time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to tidy survival fit data — h_data_plot","text":"","code":"h_data_plot(fit_km, armval = \"All\", max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to tidy survival fit data — h_data_plot","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to tidy survival fit data — h_data_plot","text":"tibble columns time, n.risk, n.event, n.censor, estimate, std.error, conf.high, conf.low, strata, censor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to tidy survival fit data — h_data_plot","text":"","code":"library(dplyr) library(survival) # Test with multiple arms tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot() #> # A tibble: 203 × 10 #> time n.risk n.event n.censor estimate std.error conf.high conf.low strata #> #> 1 0 70 0 0 1 0 1 1 ARM A #> 2 10.4 69 1 0 0.986 0.0146 1 0.958 ARM A #> 3 20.5 68 1 0 0.971 0.0208 1 0.932 ARM A #> 4 21.5 67 1 0 0.957 0.0257 1 0.910 ARM A #> 5 25.3 66 0 1 0.957 0.0257 1 0.910 ARM A #> 6 48.1 65 1 0 0.942 0.0300 0.999 0.888 ARM A #> 7 66.6 64 0 1 0.942 0.0300 0.999 0.888 ARM A #> 8 73.9 63 0 1 0.942 0.0300 0.999 0.888 ARM A #> 9 78.9 62 0 1 0.942 0.0300 0.999 0.888 ARM A #> 10 90.3 61 1 0 0.926 0.0342 0.991 0.866 ARM A #> # ℹ 193 more rows #> # ℹ 1 more variable: censor # Test with single arm tern_ex_adtte %>% filter(PARAMCD == \"OS\", ARMCD == \"ARM B\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot(armval = \"ARM B\") #> # A tibble: 74 × 10 #> time n.risk n.event n.censor estimate std.error conf.high conf.low strata #> #> 1 0 74 0 0 1 0 1 1 ARM B #> 2 2.17 73 1 0 0.986 0.0138 1 0.960 ARM B #> 3 16.9 72 1 0 0.973 0.0196 1 0.936 ARM B #> 4 22.6 71 1 0 0.959 0.0242 1 0.914 ARM B #> 5 26.3 70 1 0 0.945 0.0282 0.999 0.894 ARM B #> 6 57.5 69 1 0 0.932 0.0317 0.991 0.875 ARM B #> 7 80.0 68 1 0 0.918 0.0350 0.983 0.857 ARM B #> 8 81.6 67 1 0 0.904 0.0381 0.974 0.839 ARM B #> 9 88.6 66 1 0 0.890 0.0411 0.965 0.822 ARM B #> 10 102. 65 1 0 0.877 0.0439 0.955 0.804 ARM B #> # ℹ 64 more rows #> # ℹ 1 more variable: censor "},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":null,"dir":"Reference","previous_headings":"","what":"ggplot decomposition — h_decompose_gg","title":"ggplot decomposition — h_decompose_gg","text":"elements composing ggplot extracted organized list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggplot decomposition — h_decompose_gg","text":"","code":"h_decompose_gg(gg)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggplot decomposition — h_decompose_gg","text":"gg (ggplot) graphic decompose.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggplot decomposition — h_decompose_gg","text":"named list elements: panel: panel. yaxis: y-axis. xaxis: x-axis. xlab: x-axis label. ylab: y-axis label. guide: legend.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggplot decomposition — h_decompose_gg","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, yval = \"Survival\", censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\" ) #> Warning: `h_ggkm()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. g_el <- h_decompose_gg(gg) #> Warning: `h_decompose_gg()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"red\", fill = \"gray85\", lwd = 5)) grid::grid.draw(g_el$panel) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"royalblue\", fill = \"gray85\", lwd = 5)) grid::grid.draw(with(g_el, cbind(ylab, yaxis))) # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"","code":"h_format_row(x, format, labels = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"x (named list) list numerical values formatted optionally labeled. Elements x must numeric vectors. format (named character NULL) format patterns x. Names format must match names x. parameter passed directly rtables::format_rcell function format parameter. labels (named character NULL) optional labels x. Names labels must match names x. label specified element x, function tries use label names (order) attribute element (depending one exists NULL NA NaN). none attributes attached given element x, label automatically generated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"single row data.frame object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"","code":"mean_ci <- c(48, 51) x <- list(mean = 50, mean_ci = mean_ci) format <- c(mean = \"xx.x\", mean_ci = \"(xx.xx, xx.xx)\") labels <- c(mean = \"My Mean\") h_format_row(x, format, labels) #> My Mean V1 #> 1 50.0 (48.00, 51.00) attr(mean_ci, \"label\") <- \"Mean 95% CI\" x <- list(mean = 50, mean_ci = mean_ci) h_format_row(x, format, labels) #> My Mean Mean 95% CI #> 1 50.0 (48.00, 51.00)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create simple line plot over time — h_g_ipp","title":"Helper function to create simple line plot over time — h_g_ipp","text":"Function generates simple line plot displaying parameter trends time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create simple line plot over time — h_g_ipp","text":"","code":"h_g_ipp( df, xvar, yvar, xlab, ylab, id_var, title = \"Individual Patient Plots\", subtitle = \"\", caption = NULL, add_baseline_hline = FALSE, yvar_baseline = \"BASE\", ggtheme = nestcolor::theme_nest(), col = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create simple line plot over time — h_g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (string) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. col (character) line colors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create simple line plot over time — h_g_ipp","text":"ggplot line plot.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create simple line plot over time — h_g_ipp","text":"","code":"library(dplyr) library(nestcolor) # Select a small sample of data to plot. adlb <- tern_ex_adlb %>% filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>% slice(1:36) p <- h_g_ipp( df = adlb, xvar = \"AVISIT\", yvar = \"AVAL\", xlab = \"Visit\", id_var = \"USUBJID\", ylab = \"SGOT/ALT (U/L)\", add_baseline_hline = TRUE ) p"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a KM plot — h_ggkm","title":"Helper function to create a KM plot — h_ggkm","text":"Draw Kaplan-Meier plot using ggplot2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a KM plot — h_ggkm","text":"","code":"h_ggkm( data, xticks = NULL, yval = \"Survival\", censor_show, xlab, ylab, ylim = NULL, title, footnotes = NULL, max_time = NULL, lwd = 1, lty = NULL, pch = 3, size = 2, col = NULL, ci_ribbon = FALSE, ggtheme = nestcolor::theme_nest() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a KM plot — h_ggkm","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. yval (string) type plot, plotted y-axis. Options Survival (default) Failure probability. censor_show (flag) whether show censored observations. xlab (string) x-axis label. ylab (string) y-axis label. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. title (string) plot title. footnotes (string) plot footnotes. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL). lwd (numeric) line width. vector given, length equal number strata survival::survfit(). lty (numeric) line type. vector given, length equal number strata survival::survfit(). pch (string) name symbol character use point symbol indicate censored cases. size (numeric(1)) size censored point symbols. col (character) lines colors. Length vector equal number strata survival::survfit(). ci_ribbon (flag) whether confidence interval drawn around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 format Kaplan-Meier plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a KM plot — h_ggkm","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a KM plot — h_ggkm","text":"","code":"# \\donttest{ library(dplyr) library(survival) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", yval = \"Survival\", ylab = \"Survival Probability\", title = \"Survival\" ) gg # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for Poisson models — h_glm_count","title":"Helper functions for Poisson models — h_glm_count","text":"Helper functions returns results stats::glm() Poisson Quasi-Poisson distributions needed (see family parameter), MASS::glm.nb() Negative Binomial distributions. Link function GLM log.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for Poisson models — h_glm_count","text":"","code":"h_glm_count(.var, .df_row, variables, distribution, weights) h_glm_poisson(.var, .df_row, variables, weights) h_glm_quasipoisson(.var, .df_row, variables, weights) h_glm_negbin(.var, .df_row, variables, weights)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for Poisson models — h_glm_count","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) dataset includes variables called .var variables. variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". offset (numeric) numeric vector scalar adding offset. distribution (character) character value specifying distribution used regression (Poisson, Quasi-Poisson, negative binomial). weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for Poisson models — h_glm_count","text":"h_glm_count() returns results selected model. h_glm_poisson() returns results Poisson model. h_glm_quasipoisson() returns results Quasi-Poisson model. h_glm_negbin() returns results negative binomial model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for Poisson models — h_glm_count","text":"h_glm_count(): Helper function return results selected model (Poisson, Quasi-Poisson, negative binomial). h_glm_poisson(): Helper function return results Poisson model. h_glm_quasipoisson(): Helper function return results Quasi-Poisson model. h_glm_negbin(): Helper function return results negative binomial model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create Cox-PH grobs — h_grob_coxph","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"Grob rtable output h_tbl_coxph_pairwise()","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"","code":"h_grob_coxph( ..., x = 0, y = 0, width = grid::unit(0.4, \"npc\"), ttheme = gridExtra::ttheme_default(padding = grid::unit(c(1, 0.5), \"lines\"), core = list(bg_params = list(fill = c(\"grey95\", \"grey90\"), alpha = 0.5))) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"... arguments pass h_tbl_coxph_pairwise(). x (proportion) value 0 1 specifying x-location. y (proportion) value 0 1 specifying y-location. width (grid::unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"grob table containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) data <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) tbl_grob <- h_grob_coxph( df = data, variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\"), control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5 ) #> Warning: `h_grob_coxph()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.draw(tbl_grob) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create survival estimation grobs — h_grob_median_surv","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"survival fit transformed grob containing table groups rows characterized N, median 95% confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"","code":"h_grob_median_surv( fit_km, armval = \"All\", x = 0.9, y = 0.9, width = grid::unit(0.3, \"npc\"), ttheme = gridExtra::ttheme_default() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". x (proportion) value 0 1 specifying x-location. y (proportion) value 0 1 specifying y-location. width (grid::unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"grob table containing statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_grob_median_surv() %>% grid::grid.draw() #> Warning: `h_grob_median_surv()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"Two graphical objects obtained, one corresponding row labeling second table numbers patients risk. title = TRUE, third object corresponding table title also obtained.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"","code":"h_grob_tbl_at_risk(data, annot_tbl, xlim, title = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"data (data.frame) survival data pre-processed h_data_plot. annot_tbl (data.frame) annotation prepared survival::summary.survfit() includes number patients risk given time points. xlim (numeric(1)) maximum value x-axis (used ensure risk table aligns KM graph). title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"named list two gTree objects title = FALSE: at_risk label, three gTree objects title = TRUE: at_risk, label, title.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) # The annotation table reports the patient at risk for a given strata and # times (`xticks`). annot_tbl <- summary(fit_km, times = xticks) if (is.null(fit_km$strata)) { annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = \"All\")) } else { strata_lst <- strsplit(sub(\"=\", \"equals\", levels(annot_tbl$strata)), \"equals\") levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2] annot_tbl <- data.frame( n.risk = annot_tbl$n.risk, time = annot_tbl$time, strata = annot_tbl$strata ) } # The annotation table is transformed into a grob. tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks)) #> Warning: `h_grob_tbl_at_risk()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. # For the representation, the layout is estimated for which the decomposition # of the graphic element is necessary. g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") #> Warning: `h_km_layout()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.newpage() pushViewport(viewport(layout = lyt, height = .95, width = .95)) grid.rect(gp = grid::gpar(lty = 1, col = \"purple\", fill = \"gray85\", lwd = 1)) pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2)) grid.rect(gp = grid::gpar(lty = 1, col = \"orange\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$at_risk) popViewport() pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1)) grid.rect(gp = grid::gpar(lty = 1, col = \"green3\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$label) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"Build y-axis annotation decomposed ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"","code":"h_grob_y_annot(ylab, yaxis)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"ylab (gtable) y-lab graphical object derived ggplot. yaxis (gtable) y-axis graphical object derived ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"gTree object containing y-axis annotation ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"title\", footnotes = \"footnotes\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) grid::grid.newpage() pvp <- grid::plotViewport(margins = c(5, 4, 2, 20)) pushViewport(pvp) grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis)) #> Warning: `h_grob_y_annot()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid.rect(gp = grid::gpar(lty = 1, col = \"gray35\", fill = NA)) # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for incidence rate — h_incidence_rate","text":"","code":"h_incidence_rate(person_years, n_events, control = control_incidence_rate()) h_incidence_rate_normal(person_years, n_events, alpha = 0.05) h_incidence_rate_normal_log(person_years, n_events, alpha = 0.05) h_incidence_rate_exact(person_years, n_events, alpha = 0.05) h_incidence_rate_byar(person_years, n_events, alpha = 0.05)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for incidence rate — h_incidence_rate","text":"person_years (numeric(1)) total person-years risk. n_events (integer(1)) number events observed. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level: (proportion) confidence level estimated incidence rate. conf_type: (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit: (string)day, week, month, year (default) indicating time unit data input. num_pt_year: (numeric) time unit desired output (person-years). alpha (numeric(1)) two-sided alpha-level confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for incidence rate — h_incidence_rate","text":"Estimated incidence rate, rate, associated confidence interval, rate_ci.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for incidence rate — h_incidence_rate","text":"h_incidence_rate(): Helper function estimate incidence rate associated confidence interval. h_incidence_rate_normal(): Helper function estimate incidence rate associated confidence interval based normal approximation incidence rate. Unit one person-year. h_incidence_rate_normal_log(): Helper function estimate incidence rate associated confidence interval based normal approximation logarithm incidence rate. Unit one person-year. h_incidence_rate_exact(): Helper function estimate incidence rate associated exact confidence interval. Unit one person-year. h_incidence_rate_byar(): Helper function estimate incidence rate associated Byar's confidence interval. Unit one person-year.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for incidence rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] -0.003859038 0.023859038 #> h_incidence_rate_normal_log(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.002500977 0.039984382 #> h_incidence_rate_exact(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.001211046 0.036123438 #> h_incidence_rate_byar(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.001994207 0.032054171 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare a KM layout — h_km_layout","title":"Helper function to prepare a KM layout — h_km_layout","text":"Prepares (5 rows) x (2 cols) layout Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare a KM layout — h_km_layout","text":"","code":"h_km_layout( data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare a KM layout — h_km_layout","text":"data (data.frame) survival data pre-processed h_data_plot. g_el (list gtable) list obtained h_decompose_gg(). title (string) plot title. footnotes (string) plot footnotes. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare a KM layout — h_km_layout","text":"grid layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function to prepare a KM layout — h_km_layout","text":"layout corresponds grid two columns five rows unequal dimensions. dimension fixed, curve flexible accommodate remaining free space. left column gets annotation ggplot (y-axis) names strata patient risk tabulation. main constraint width columns must allow writing strata name. right column receive ggplot, legend, x-axis patient risk table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare a KM layout — h_km_layout","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") grid.show.layout(lyt) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for multivariate logistic regression — h_logistic_regression","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"Helper functions used calculations logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"","code":"h_get_interaction_vars(fit_glm) h_interaction_coef_name( interaction_vars, first_var_with_level, second_var_with_level ) h_or_cat_interaction( odds_ratio_var, interaction_var, fit_glm, conf_level = 0.95 ) h_or_cont_interaction( odds_ratio_var, interaction_var, fit_glm, at = NULL, conf_level = 0.95 ) h_or_interaction( odds_ratio_var, interaction_var, fit_glm, at = NULL, conf_level = 0.95 ) h_simple_term_labels(terms, table) h_interaction_term_labels(terms1, terms2, table, any = FALSE) h_glm_simple_term_extract(x, fit_glm) h_glm_interaction_extract(x, fit_glm) h_glm_inter_term_extract(odds_ratio_var, interaction_var, fit_glm, ...) h_logistic_simple_terms(x, fit_glm, conf_level = 0.95) h_logistic_inter_terms(x, fit_glm, conf_level = 0.95, at = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"fit_glm (glm) logistic regression model fitted stats::glm() \"binomial\" family. Limited functionality also available conditional logistic regression models fitted survival::clogit(), currently used extract_rsp_biomarkers(). interaction_vars (character(2)) interaction variable names. first_var_with_level (character(2)) first variable name interaction level. second_var_with_level (character(2)) second variable name interaction level. odds_ratio_var (string) odds ratio variable. interaction_var (string) interaction variable. conf_level (proportion) confidence level interval. (numeric NULL) optional values interaction variable. Otherwise median used. terms (character) simple terms. table (table) table containing numbers terms. terms1 (character) terms first dimension (rows). terms2 (character) terms second dimension (rows). (flag) whether term1 term2 can fulfilled count number patients. case can scalar (strings). x (character) variable interaction term fit_glm (depending helper function used). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"Vector names interaction variables. Name coefficient. Odds ratio. Odds ratio. Odds ratio. Term labels containing numbers patients. Term labels containing numbers patients. Tabulated main effect results logistic regression model. Tabulated interaction term results logistic regression model. data.frame tabulated interaction term results logistic regression model. Tabulated statistics given variable(s) logistic regression model. Tabulated statistics given variable(s) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"h_get_interaction_vars(): Helper function extract interaction variable names fitted model assuming one interaction term. h_interaction_coef_name(): Helper function get right coefficient name interaction variable names given levels. main value order first second variable checked interaction_vars input. h_or_cat_interaction(): Helper function calculate odds ratio estimates case odds ratio interaction variable categorical. h_or_cont_interaction(): Helper function calculate odds ratio estimates case either odds ratio interaction variable continuous. h_or_interaction(): Helper function calculate odds ratio estimates case interaction. wrapper h_or_cont_interaction() h_or_cat_interaction(). h_simple_term_labels(): Helper function construct term labels simple terms table numbers patients. h_interaction_term_labels(): Helper function construct term labels interaction terms table numbers patients. h_glm_simple_term_extract(): Helper function tabulate main effect results (conditional) logistic regression model. h_glm_interaction_extract(): Helper function tabulate interaction term results logistic regression model. h_glm_inter_term_extract(): Helper function tabulate interaction results logistic regression model. basically wrapper h_or_interaction() h_glm_simple_term_extract() puts results right data frame format. h_logistic_simple_terms(): Helper function tabulate results including odds ratios confidence intervals simple terms. h_logistic_inter_terms(): Helper function tabulate results including odds ratios confidence intervals interaction terms.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"provide function case variables continuous arise table, treatment arm variable always involved categorical.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"","code":"library(dplyr) library(broom) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>% mutate( Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), RACE = factor(RACE), SEX = factor(SEX) ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\") ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\"), interaction = \"AGE\" ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred h_glm_simple_term_extract(\"AGE\", mod1) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> reference reference_label estimate std_error df pvalue #> 1 0.1698216 0.09524116 1 0.07457501 #> is_variable_summary is_term_summary #> 1 FALSE TRUE h_glm_simple_term_extract(\"ARMCD\", mod1) #> variable variable_label term term_label interaction #> 1 ARMCD Planned Arm Code ARM A Reference ARM A, n = 64 #> 2 ARMCD Planned Arm Code ARM B ARM B, n = 68 #> 3 ARMCD Planned Arm Code ARM C ARM C, n = 52 #> interaction_label reference reference_label estimate std_error df pvalue #> 1 2 0.3004308 #> 2 -1.774769 1.144405 1 0.1209443 #> 3 17.1922 3626.588 1 0.9962176 #> is_variable_summary is_term_summary #> 1 TRUE FALSE #> 2 FALSE TRUE #> 3 FALSE TRUE h_glm_interaction_extract(\"ARMCD:AGE\", mod2) #> variable variable_label term term_label #> 1 ARMCD:AGE Interaction of Planned Arm Code * Age ARM A Reference ARM A, n = 64 #> 2 ARMCD:AGE Interaction of Planned Arm Code * Age ARM B ARM B, n = 68 #> 3 ARMCD:AGE Interaction of Planned Arm Code * Age ARM C ARM C, n = 52 #> interaction interaction_label reference reference_label estimate std_error #> 1 #> 2 0.3081205 0.2062392 #> 3 0.02948826 548.5923 #> df pvalue is_variable_summary is_term_summary #> 1 2 0.3275837 TRUE FALSE #> 2 1 0.1351767 FALSE TRUE #> 3 1 0.9999571 FALSE TRUE h_glm_inter_term_extract(\"AGE\", \"ARMCD\", mod2) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> 2 AGE Age AGE Age ARMCD Planned Arm Code #> 3 AGE Age AGE Age ARMCD Planned Arm Code #> 4 AGE Age AGE Age ARMCD Planned Arm Code #> reference reference_label estimate std_error odds_ratio lcl ucl #> 1 -0.03873898 0.1514322 NA NA NA #> 2 ARM A ARM A NA NA 0.9620018 0.7149514 1.294420 #> 3 ARM B ARM B NA NA 1.3091545 1.0021802 1.710157 #> 4 ARM C ARM C NA NA 0.9907919 0.0000000 Inf #> df pvalue is_variable_summary is_term_summary is_reference_summary #> 1 1 0.798092 FALSE TRUE FALSE #> 2 NA NA FALSE FALSE TRUE #> 3 NA NA FALSE FALSE TRUE #> 4 NA NA FALSE FALSE TRUE h_logistic_simple_terms(\"AGE\", mod1) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> reference reference_label estimate std_error df pvalue #> 1 0.1698216 0.09524116 1 0.07457501 #> is_variable_summary is_term_summary odds_ratio lcl ucl #> 1 FALSE TRUE 1.185093 0.9832935 1.428308 #> ci #> 1 0.9832935, 1.4283084 h_logistic_inter_terms(c(\"RACE\", \"AGE\", \"ARMCD\", \"AGE:ARMCD\"), mod2) #> variable variable_label term #> 1 RACE Race ASIAN #> 2 RACE Race BLACK OR AFRICAN AMERICAN #> 3 RACE Race WHITE #> 13 ARMCD Planned Arm Code ARM A #> 23 ARMCD Planned Arm Code ARM B #> ARM B ARMCD Planned Arm Code ARM B #> 33 ARMCD Planned Arm Code ARM C #> ARM C ARMCD Planned Arm Code ARM C #> 11 AGE Age AGE #> 21 AGE Age AGE #> 31 AGE Age AGE #> 4 AGE Age AGE #> 12 AGE:ARMCD Interaction of Planned Arm Code * Age ARM A #> 22 AGE:ARMCD Interaction of Planned Arm Code * Age ARM B #> 32 AGE:ARMCD Interaction of Planned Arm Code * Age ARM C #> term_label interaction interaction_label reference #> 1 Reference ASIAN, n = 110 #> 2 BLACK OR AFRICAN AMERICAN, n = 40 #> 3 WHITE, n = 34 #> 13 Reference ARM A, n = 64 #> 23 ARM B, n = 68 #> ARM B ARM B, n = 68 AGE Age 35 #> 33 ARM C, n = 52 #> ARM C ARM C, n = 52 AGE Age 35 #> 11 Age #> 21 Age ARMCD Planned Arm Code ARM A #> 31 Age ARMCD Planned Arm Code ARM B #> 4 Age ARMCD Planned Arm Code ARM C #> 12 Reference ARM A, n = 64 #> 22 ARM B, n = 68 #> 32 ARM C, n = 52 #> reference_label estimate std_error df pvalue odds_ratio lcl #> 1 2 0.9361139 #> 2 18.15018 3944.701 1 0.9963288 76299564 0 #> 3 -0.3727152 1.025808 1 0.7163522 0.6888614 0.09224926 #> 13 2 0.253914 NA NA #> 23 -11.52715 6.96188 1 0.09777185 NA NA #> ARM B 35 NA NA NA NA 0.4757148 0.03361147 #> 33 16.33327 20278.39 1 0.9993573 NA NA #> ARM C 35 NA NA NA NA 34808099 0 #> 11 -0.03873898 0.1514322 1 0.798092 NA NA #> 21 ARM A NA NA NA NA 0.9620018 0.7149514 #> 31 ARM B NA NA NA NA 1.309155 1.00218 #> 4 ARM C NA NA NA NA 0.9907919 0 #> 12 NA NA NA NA #> 22 0.3081205 0.2062392 1 0.1351767 NA NA #> 32 0.02948826 548.5923 1 0.9999571 NA NA #> ucl is_variable_summary is_term_summary is_reference_summary #> 1 TRUE FALSE FALSE #> 2 Inf FALSE TRUE FALSE #> 3 5.143998 FALSE TRUE FALSE #> 13 NA TRUE FALSE FALSE #> 23 NA FALSE TRUE FALSE #> ARM B 6.732956 FALSE FALSE TRUE #> 33 NA FALSE TRUE FALSE #> ARM C Inf FALSE FALSE TRUE #> 11 NA FALSE TRUE FALSE #> 21 1.29442 FALSE FALSE TRUE #> 31 1.710157 FALSE FALSE TRUE #> 4 Inf FALSE FALSE TRUE #> 12 NA TRUE FALSE FALSE #> 22 NA FALSE TRUE FALSE #> 32 NA FALSE TRUE FALSE #> ci #> 1 #> 2 0, Inf #> 3 0.09224926, 5.14399810 #> 13 NA, NA #> 23 NA, NA #> ARM B 0.03361147, 6.73295611 #> 33 NA, NA #> ARM C 0, Inf #> 11 NA, NA #> 21 0.7149514, 1.2944200 #> 31 1.002180, 1.710157 #> 4 0, Inf #> 12 NA, NA #> 22 NA, NA #> 32 NA, NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"Helper function create map data frame input dataset, can used argument trim_levels_to_map split function. Based different method, map constructed differently.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"","code":"h_map_for_count_abnormal( df, variables = list(anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\", range_high = \"ANRHI\"), abnormal = list(low = c(\"LOW\", \"LOW LOW\"), high = c(\"HIGH\", \"HIGH HIGH\")), method = c(\"default\", \"range\"), na_str = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"df (data.frame) data set containing analysis variables. variables (named list string) list additional analysis variables. abnormal (named list) identifying abnormal range level(s) df. Based levels abnormality input dataset, can something like list(Low = \"LOW LOW\", High = \"HIGH HIGH\") abnormal = list(Low = \"LOW\", High = \"HIGH\")) method (string) indicates returned map constructed. Can \"default\" \"range\". na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"map data.frame.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"method \"default\", returned map abnormal directions observed df, records normal values excluded avoid error creating layout. method \"range\", returned map based rule least one observation low range > 0 low direction least one observation high range missing high direction.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"","code":"adlb <- df_explicit_na(tern_ex_adlb) h_map_for_count_abnormal( df = adlb, variables = list(anl = \"ANRIND\", split_rows = c(\"LBCAT\", \"PARAM\")), abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")), method = \"default\", na_str = \"\" ) #> LBCAT PARAM ANRIND #> 1 CHEMISTRY Alanine Aminotransferase Measurement LOW #> 4 CHEMISTRY Alanine Aminotransferase Measurement HIGH #> 7 CHEMISTRY Alanine Aminotransferase Measurement NORMAL #> 2 CHEMISTRY C-Reactive Protein Measurement LOW #> 3 CHEMISTRY C-Reactive Protein Measurement HIGH #> 8 CHEMISTRY C-Reactive Protein Measurement NORMAL #> 5 IMMUNOLOGY Immunoglobulin A Measurement LOW #> 6 IMMUNOLOGY Immunoglobulin A Measurement HIGH #> 9 IMMUNOLOGY Immunoglobulin A Measurement NORMAL df <- data.frame( USUBJID = c(rep(\"1\", 4), rep(\"2\", 4), rep(\"3\", 4)), AVISIT = c( rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2), rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2), rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2) ), PARAM = rep(c(\"ALT\", \"CPR\"), 6), ANRIND = c( \"NORMAL\", \"NORMAL\", \"LOW\", \"HIGH\", \"LOW\", \"LOW\", \"HIGH\", \"HIGH\", rep(\"NORMAL\", 4) ), ANRLO = rep(5, 12), ANRHI = rep(20, 12) ) df$ANRIND <- factor(df$ANRIND, levels = c(\"LOW\", \"HIGH\", \"NORMAL\")) h_map_for_count_abnormal( df = df, variables = list( anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\", range_high = \"ANRHI\" ), abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")), method = \"range\", na_str = \"\" ) #> PARAM ANRIND #> 1 ALT LOW #> 3 ALT HIGH #> 5 ALT NORMAL #> 2 CPR LOW #> 4 CPR HIGH #> 6 CPR NORMAL"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for odds ratio estimation — h_odds_ratio","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"Functions calculate odds ratios estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"","code":"or_glm(data, conf_level) or_clogit(data, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"data (data.frame) data frame containing least variables rsp grp, optionally strata or_clogit(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"named list elements or_ci n_tot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"or_glm(): Estimates odds ratio based stats::glm(). Note must exactly 2 groups data specified grp variable. or_clogit(): Estimates odds ratio based survival::clogit(). done whole data set including groups, since results pairwise comparisons groups.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"","code":"# Data with 2 groups. data <- data.frame( rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)), grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)], strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)], stringsAsFactors = TRUE ) # Odds ratio based on glm. or_glm(data, conf_level = 0.95) #> $or_ci #> est lcl ucl #> 0.33333333 0.01669735 6.65441589 #> #> $n_tot #> n_tot #> 8 #> # Data with 3 groups. data <- data.frame( rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)), grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)], strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)], stringsAsFactors = TRUE ) # Odds ratio based on stratified estimation by conditional logistic regression. or_clogit(data, conf_level = 0.95) #> $or_ci #> $or_ci$b #> est lcl ucl #> 0.28814553 0.02981009 2.78522598 #> #> $or_ci$c #> est lcl ucl #> 0.5367919 0.0673365 4.2791881 #> #> #> $n_tot #> n_tot #> 20 #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"","code":"h_pkparam_sort(pk_data, key_var = \"PARAMCD\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"pk_data (data.frame) pharmacokinetic data frame. key_var (string) key variable used merge pk_data metadata created d_pkparam().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"pharmacokinetic data.frame sorted PARAM variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"","code":"library(dplyr) adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, \" (\", AVALU, \")\"))) pk_ordered_data <- h_pkparam_sort(adpp)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to return the estimated means using predicted probabilities — h_ppmeans","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"arm level, predicted mean rate calculated using fitted model object, newdata set result stats::model.frame, reconstructed data original data, depending object formula (coming fit). confidence interval derived using conf_level parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"","code":"h_ppmeans(obj, .df_row, arm, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"obj (glm.fit) fitted model object used derive mean rate estimates treatment arm. .df_row (data.frame) dataset includes variables called .var variables. arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. conf_level (proportion) value used derive confidence interval rate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"h_ppmeans() returns estimated means.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"","code":"prop_diff_wald(rsp, grp, conf_level = 0.95, correct = FALSE) prop_diff_ha(rsp, grp, conf_level) prop_diff_nc(rsp, grp, conf_level, correct = FALSE) prop_diff_cmh(rsp, grp, strata, conf_level = 0.95) prop_diff_strat_nc( rsp, grp, strata, weights_method = c(\"cmh\", \"wilson_h\"), conf_level = 0.95, correct = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"rsp (logical) vector indicating whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). conf_level (proportion) confidence level interval. correct (flag) whether include continuity correction. information, see stats::prop.test(). strata (factor) variable one level per stratum length rsp. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"named list elements diff (proportion difference) diff_ci (proportion difference confidence interval).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"prop_diff_wald(): Wald interval follows usual textbook definition single proportion confidence interval using normal approximation. possible include continuity correction Wald's interval. prop_diff_ha(): Anderson-Hauck confidence interval. prop_diff_nc(): Newcombe confidence interval. based Wilson score confidence interval single binomial proportion. prop_diff_cmh(): Calculates weighted difference. defined difference response rates experimental treatment group control treatment group, adjusted stratification factors applying Cochran-Mantel-Haenszel (CMH) weights. CMH chi-squared test, use stats::mantelhaen.test(). prop_diff_strat_nc(): Calculates stratified Newcombe confidence interval difference response rates experimental treatment group control treatment group, adjusted stratification factors. implementation follows closely one proposed Yan Su (2010) . Weights can estimated heuristic proposed prop_strat_wilson() CMH-derived weights (see prop_diff_cmh()).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329–335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"","code":"# Wald confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20) grp <- factor(c(rep(\"A\", 10), rep(\"B\", 10))) prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE) #> $diff #> [1] 0 #> #> $diff_ci #> [1] -0.4382613 0.4382613 #> # Anderson-Hauck confidence interval ## \"Mid\" case: 3/4 respond in group A, 1/2 respond in group B. rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) grp <- factor(c(\"A\", \"B\", \"A\", \"B\", \"A\", \"A\"), levels = c(\"B\", \"A\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90) #> $diff #> [1] 0.25 #> #> $diff_ci #> [1] -0.9195011 1.0000000 #> ## Edge case: Same proportion of response in A and B. rsp <- c(TRUE, FALSE, TRUE, FALSE) grp <- factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6) #> $diff #> [1] 0 #> #> $diff_ci #> [1] -0.8451161 0.8451161 #> # Newcombe confidence interval set.seed(1) rsp <- c( sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE), sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE) ) grp <- factor(rep(c(\"A\", \"B\"), each = 40), levels = c(\"B\", \"A\")) table(rsp, grp) #> grp #> rsp B A #> FALSE 20 10 #> TRUE 20 30 prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9) #> $diff #> [1] 0.25 #> #> $diff_ci #> [1] 0.07193388 0.40725819 #> # Cochran-Mantel-Haenszel confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), 100, TRUE) grp <- sample(c(\"Placebo\", \"Treatment\"), 100, TRUE) grp <- factor(grp, levels = c(\"Placebo\", \"Treatment\")) strata_data <- data.frame( \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE ) prop_diff_cmh( rsp = rsp, grp = grp, strata = interaction(strata_data), conf_level = 0.90 ) #> $prop #> Placebo Treatment #> 0.5331117 0.3954251 #> #> $prop_ci #> $prop_ci$Placebo #> [1] 0.4306536 0.6355698 #> #> $prop_ci$Treatment #> [1] 0.2890735 0.5017768 #> #> #> $diff #> [1] -0.1376866 #> #> $diff_ci #> [1] -0.285363076 0.009989872 #> #> $weights #> a.x b.x a.y b.y a.z b.z #> 0.1148388 0.2131696 0.1148388 0.2131696 0.1767914 0.1671918 #> #> $n1 #> a.x b.x a.y b.y a.z b.z #> 4 11 8 11 13 11 #> #> $n2 #> a.x b.x a.y b.y a.z b.z #> 8 9 4 9 6 6 #> # Stratified Newcombe confidence interval set.seed(2) data_set <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), 100, TRUE), \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), \"grp\" = sample(c(\"Placebo\", \"Treatment\"), 100, TRUE), stringsAsFactors = TRUE ) prop_diff_strat_nc( rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]), weights_method = \"cmh\", conf_level = 0.90 ) #> $diff #> [1] -0.05777672 #> #> $diff_ci #> lower upper #> -0.2236537 0.1119331 #> prop_diff_strat_nc( rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]), weights_method = \"wilson_h\", conf_level = 0.90 ) #> $diff #> [1] -0.07771884 #> #> $diff_ci #> lower upper #> -0.2540844 0.1027720 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions to test proportion differences — h_prop_diff_test","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"Helper functions implement various tests difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"","code":"prop_chisq(tbl) prop_cmh(ary) prop_schouten(tbl) prop_fisher(tbl)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"tbl (matrix) matrix two groups rows binary response (TRUE/FALSE) columns. ary (array, 3 dimensions) array two groups rows, binary response (TRUE/FALSE) columns, strata third dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"prop_chisq(): Performs Chi-Squared test. Internally calls stats::prop.test(). prop_cmh(): Performs stratified Cochran-Mantel-Haenszel test. Internally calls stats::mantelhaen.test(). Note strata less two observations automatically discarded. prop_schouten(): Performs Chi-Squared test Schouten correction. prop_fisher(): Performs Fisher's exact test. Internally calls stats::fisher.test().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for calculating proportion confidence intervals — h_proportions","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Functions calculate different proportion confidence intervals use estimate_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"","code":"prop_wilson(rsp, conf_level, correct = FALSE) prop_strat_wilson( rsp, strata, weights = NULL, conf_level = 0.95, max_iterations = NULL, correct = FALSE ) prop_clopper_pearson(rsp, conf_level) prop_wald(rsp, conf_level, correct = FALSE) prop_agresti_coull(rsp, conf_level) prop_jeffreys(rsp, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"rsp (logical) vector indicating whether subject responder . conf_level (proportion) confidence level interval. correct (flag) whether apply continuity correction. strata (factor) variable one level per stratum length rsp. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Confidence interval proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"prop_wilson(): Calculates Wilson interval calling stats::prop.test(). Also referred Wilson score interval. prop_strat_wilson(): Calculates stratified Wilson confidence interval unequal proportions described Yan Su (2010) prop_clopper_pearson(): Calculates Clopper-Pearson interval calling stats::binom.test(). Also referred exact method. prop_wald(): Calculates Wald interval following usual textbook definition single proportion confidence interval using normal approximation. prop_agresti_coull(): Calculates Agresti-Coull interval. Constructed (95% CI) adding two successes two failures data using Wald formula construct CI. prop_jeffreys(): Calculates Jeffreys interval, equal-tailed interval based non-informative Jeffreys prior binomial proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329–335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"","code":"rsp <- c( TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE ) prop_wilson(rsp, conf_level = 0.9) #> [1] 0.2692718 0.7307282 # Stratified Wilson confidence interval with unequal probabilities set.seed(1) rsp <- sample(c(TRUE, FALSE), 100, TRUE) strata_data <- data.frame( \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE ) strata <- interaction(strata_data) n_strata <- ncol(table(rsp, strata)) # Number of strata prop_strat_wilson( rsp = rsp, strata = strata, conf_level = 0.90 ) #> $conf_int #> lower upper #> 0.4072891 0.5647887 #> #> $weights #> a.x b.x a.y b.y a.z b.z #> 0.2074199 0.1776464 0.1915610 0.1604678 0.1351096 0.1277952 #> # Not automatic setting of weights prop_strat_wilson( rsp = rsp, strata = strata, weights = rep(1 / n_strata, n_strata), conf_level = 0.90 ) #> $conf_int #> lower upper #> 0.4190436 0.5789733 #> prop_clopper_pearson(rsp, conf_level = .95) #> [1] 0.3886442 0.5919637 prop_wald(rsp, conf_level = 0.95) #> [1] 0.3920214 0.5879786 prop_wald(rsp, conf_level = 0.95, correct = TRUE) #> [1] 0.3870214 0.5929786 prop_agresti_coull(rsp, conf_level = 0.95) #> [1] 0.3942193 0.5865206 prop_jeffreys(rsp, conf_level = 0.95) #> [1] 0.3934779 0.5870917"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"","code":"h_rsp_to_logistic_variables(variables, biomarker) h_logistic_mult_cont_df(variables, data, control = control_logistic()) h_tab_rsp_one_biomarker(df, vars, na_str = default_na_str(), .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (named list) controls response definition confidence level produced control_logistic(). df (data.frame) results single biomarker, part returned extract_rsp_biomarkers() (needs couple columns added high-level function relative returned h_logistic_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables() returns named list elements response, arm, covariates, strata. h_logistic_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_rsp_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables(): helps converting \"response\" function variable list \"logistic regression\" variable list. reason currently inconsistency variable names accepted extract_rsp_subgroups() fit_logistic(). h_logistic_mult_cont_df(): prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates strata. h_tab_rsp_one_biomarker(): Prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") # This is how the variable list is converted internally. h_rsp_to_logistic_variables( variables = list( rsp = \"RSP\", covariates = c(\"A\", \"B\"), strata = \"D\" ), biomarker = \"AGE\" ) #> $response #> [1] \"RSP\" #> #> $arm #> [1] \"AGE\" #> #> $covariates #> [1] \"A\" \"B\" #> #> $strata #> [1] \"D\" #> # For a single population, estimate separately the effects # of two biomarkers. df <- h_logistic_mult_cont_df( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\" ), data = adrs_f ) df #> biomarker biomarker_label n_tot n_rsp prop or lcl #> 1 BMRKR1 Continuous Level Biomarker 1 200 164 0.82 0.9755036 0.8804862 #> 2 AGE Age 200 164 0.82 0.9952416 0.9462617 #> ucl conf_level pval pval_label #> 1 1.080775 0.95 0.6352602 p-value (Wald) #> 2 1.046757 0.95 0.8530389 p-value (Wald) # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", strata = \"STRATA1\" ), data = adrs_f[NULL, ] ) #> biomarker biomarker_label n_tot n_tot_events median hr lcl ucl #> 1 BMRKR1 Continuous Level Biomarker 1 0 0 NA NA NA NA #> 2 AGE Age 0 0 NA NA NA NA #> conf_level pval pval_label #> 1 0.95 NA p-value (Wald) #> 2 0.95 NA p-value (Wald) # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_rsp_one_biomarker( df1, vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\") ) #> Total n Responders Response (%) Odds Ratio 95% CI p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> All patients 200 164 82.0% 0.98 (0.88, 1.08) 0.6353"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"Helper functions tabulate data frame statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"","code":"h_proportion_df(rsp, arm) h_proportion_subgroups_df( variables, data, groups_lists = list(), label_all = \"All Patients\" ) h_odds_ratio_df(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) h_odds_ratio_subgroups_df( variables, data, groups_lists = list(), conf_level = 0.95, method = NULL, label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"rsp (logical) vector indicating whether subject responder . arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame, NULL) required stratified analysis performed. conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"h_proportion_df() returns data.frame columns arm, n, n_rsp, prop. h_proportion_subgroups_df() returns data.frame columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. h_odds_ratio_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, optionally pval pval_label. h_odds_ratio_subgroups_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"h_proportion_df(): Helper prepare data frame binary responses arm. h_proportion_subgroups_df(): Summarizes proportion binary responses arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_odds_ratio_df(): Helper prepare data frame estimates odds ratio treatment control arm. h_odds_ratio_subgroups_df(): Summarizes estimates odds ratio treatment control arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") h_proportion_df( c(TRUE, FALSE, FALSE), arm = factor(c(\"A\", \"A\", \"B\"), levels = c(\"A\", \"B\")) ) #> arm n n_rsp prop #> 1 A 2 1 0.5 #> 2 B 1 0 0.0 h_proportion_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Define groupings for BMRKR2 levels. h_proportion_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 low BMRKR2 #> 8 A: Drug X 26 21 0.8076923 low BMRKR2 #> 9 B: Placebo 47 30 0.6382979 low/medium BMRKR2 #> 10 A: Drug X 52 44 0.8461538 low/medium BMRKR2 #> 11 B: Placebo 73 50 0.6849315 low/medium/high BMRKR2 #> 12 A: Drug X 69 59 0.8550725 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Unstratatified analysis. h_odds_ratio_df( c(TRUE, FALSE, FALSE, TRUE), arm = factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) ) #> arm n_tot or lcl ucl conf_level #> 1 4 1 0.01984252 50.39681 0.95 # Include p-value. h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = \"chisq\") #> arm n_tot or lcl ucl conf_level pval #> 1 142 2.714 1.180449 6.239827 0.95 0.01643036 #> pval_label #> 1 p-value (Chi-Squared Test) # Stratatified analysis. h_odds_ratio_df( rsp = adrs_f$rsp, arm = adrs_f$ARM, strata_data = adrs_f[, c(\"STRATA1\", \"STRATA2\")], method = \"cmh\" ) #> arm n_tot or lcl ucl conf_level pval #> 1 142 2.665586 1.146149 6.199324 0.95 0.02019665 #> pval_label #> 1 p-value (Cochran-Mantel-Haenszel Test) # Unstratified analysis. h_odds_ratio_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.1804488 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.2669576 51.452218 0.95 F SEX #> 3 64 0.920000 0.2966470 2.853223 0.95 M SEX #> 4 50 3.553846 1.0047370 12.570277 0.95 LOW BMRKR2 #> 5 49 2.705882 0.5911718 12.385232 0.95 MEDIUM BMRKR2 #> 6 43 2.250000 0.3970298 12.750933 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis # Stratified analysis. h_odds_ratio_subgroups_df( variables = list( rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = c(\"STRATA1\", \"STRATA2\") ), data = adrs_f ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.6655860 1.1461490 6.199324 0.95 All Patients ALL #> 2 78 7.7065093 1.5817529 37.547132 0.95 F SEX #> 3 64 0.9572284 0.2990954 3.063525 0.95 M SEX #> 4 50 3.0323726 0.8833232 10.409875 0.95 LOW BMRKR2 #> 5 49 2.1264996 0.4312008 10.486995 0.95 MEDIUM BMRKR2 #> 6 43 2.5134820 0.4351747 14.517370 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis # Define groupings of BMRKR2 levels. h_odds_ratio_subgroups_df( variables = list( rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.180449 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.266958 51.452218 0.95 F SEX #> 3 64 0.920000 0.296647 2.853223 0.95 M SEX #> 4 50 3.553846 1.004737 12.570277 0.95 low BMRKR2 #> 5 99 3.116667 1.193409 8.139385 0.95 low/medium BMRKR2 #> 6 142 2.714000 1.180449 6.239827 0.95 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data frame by subgroups — h_split_by_subgroups","title":"Split data frame by subgroups — h_split_by_subgroups","text":"Split data frame non-nested list subsets.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data frame by subgroups — h_split_by_subgroups","text":"","code":"h_split_by_subgroups(data, subgroups, groups_lists = list())"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data frame by subgroups — h_split_by_subgroups","text":"data (data.frame) dataset split. subgroups (character) names factor variables data used create subsets. Unused levels present data dropped. Note order vector determines order downstream table. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data frame by subgroups — h_split_by_subgroups","text":"list subset data (df) metadata subset (df_labels).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split data frame by subgroups — h_split_by_subgroups","text":"Main functionality prepare data use forest plot layouts.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data frame by subgroups — h_split_by_subgroups","text":"","code":"df <- data.frame( x = c(1:5), y = factor(c(\"A\", \"B\", \"A\", \"B\", \"A\"), levels = c(\"A\", \"B\", \"C\")), z = factor(c(\"C\", \"C\", \"D\", \"D\", \"D\"), levels = c(\"D\", \"C\")) ) formatters::var_labels(df) <- paste(\"label for\", names(df)) h_split_by_subgroups( data = df, subgroups = c(\"y\", \"z\") ) #> $y.A #> $y.A$df #> x y z #> 1 1 A C #> 2 3 A D #> 3 5 A D #> #> $y.A$df_labels #> subgroup var var_label #> 1 A y label for y #> #> #> $y.B #> $y.B$df #> x y z #> 1 2 B C #> 2 4 B D #> #> $y.B$df_labels #> subgroup var var_label #> 1 B y label for y #> #> #> $z.D #> $z.D$df #> x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #> #> $z.D$df_labels #> subgroup var var_label #> 1 D z label for z #> #> #> $z.C #> $z.C$df #> x y z #> 1 1 A C #> 2 2 B C #> #> $z.C$df_labels #> subgroup var var_label #> 1 C z label for z #> #> h_split_by_subgroups( data = df, subgroups = c(\"y\", \"z\"), groups_lists = list( y = list(\"AB\" = c(\"A\", \"B\"), \"C\" = \"C\") ) ) #> $y.AB #> $y.AB$df #> x y z #> 1 1 A C #> 2 2 B C #> 3 3 A D #> 4 4 B D #> 5 5 A D #> #> $y.AB$df_labels #> subgroup var var_label #> 1 AB y label for y #> #> #> $z.D #> $z.D$df #> x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #> #> $z.D$df_labels #> subgroup var var_label #> 1 D z label for z #> #> #> $z.C #> $z.C$df #> x y z #> 1 1 A C #> 2 2 B C #> #> $z.C$df_labels #> subgroup var var_label #> 1 C z label for z #> #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Split parameters — h_split_param","title":"Split parameters — h_split_param","text":"divides data vector param groups defined f based specified values. relevant rtables layers distribute parameters .stats ' .formats lists items corresponding specific analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split parameters — h_split_param","text":"","code":"h_split_param(param, value, f)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split parameters — h_split_param","text":"param (vector) parameter split. value (vector) value used split. f (list) reference make split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split parameters — h_split_param","text":"named list element names f, containing elements specified .stats.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split parameters — h_split_param","text":"","code":"f <- list( surv = c(\"pt_at_risk\", \"event_free_rate\", \"rate_se\", \"rate_ci\"), surv_diff = c(\"rate_diff\", \"rate_diff_ci\", \"ztest_pval\") ) .stats <- c(\"pt_at_risk\", \"rate_diff\") h_split_param(.stats, .stats, f = f) #> $surv #> [1] \"pt_at_risk\" #> #> $surv_diff #> [1] \"rate_diff\" #> # $surv # [1] \"pt_at_risk\" # # $surv_diff # [1] \"rate_diff\" .formats <- c(\"pt_at_risk\" = \"xx\", \"event_free_rate\" = \"xxx\") h_split_param(.formats, names(.formats), f = f) #> $surv #> pt_at_risk event_free_rate #> \"xx\" \"xxx\" #> #> $surv_diff #> NULL #> # $surv # pt_at_risk event_free_rate # \"xx\" \"xxx\" # # $surv_diff # NULL"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"Helper function create new SMQ variable ADAE consists adverse events belonging selected Standardized/Customized queries. new dataset contain records adverse events belonging selected baskets. Remember na_str must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"h_stack_by_baskets( df, baskets = grep(\"^(SMQ|CQ).+NAM$\", names(df), value = TRUE), smq_varlabel = \"Standardized MedDRA Query\", keys = c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"AEDECOD\", \"AESEQ\"), aag_summary = NULL, na_str = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"df (data.frame) data set containing analysis variables. baskets (character) variable names selected Standardized/Customized queries. smq_varlabel (string) label new variable created. keys (character) names key variables returned along new variable created. aag_summary (data.frame) containing SMQ baskets levels interest final SMQ variable. useful levels interest observed df dataset. two columns dataset named basket basket_name. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"data.frame variables keys taken df new variable SMQ containing records belonging baskets selected via baskets argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"adae <- tern_ex_adae[1:20, ] %>% df_explicit_na() h_stack_by_baskets(df = adae) #> # A tibble: 8 × 6 #> STUDYID USUBJID ASTDTM AEDECOD AESEQ SMQ #> #> 1 AB12345 AB12345-BRA-11-id-8 2021-12-05 02:02:07 dcd D.2.1.5.3 2 D.2.1.5… #> 2 AB12345 AB12345-BRA-12-id-120 2020-02-05 01:42:29 dcd D.2.1.5.3 2 D.2.1.5… #> 3 AB12345 AB12345-BRA-1-id-171 2022-11-29 12:18:31 dcd C.1.1.1.3 2 C.1.1.1… #> 4 AB12345 AB12345-BRA-1-id-23 2020-07-10 07:32:49 dcd B.2.2.3.1 3 C.1.1.1… #> 5 AB12345 AB12345-BRA-1-id-59 2021-10-10 23:54:46 dcd C.1.1.1.3 4 C.1.1.1… #> 6 AB12345 AB12345-BRA-1-id-9 2021-06-01 14:39:09 dcd C.1.1.1.3 1 C.1.1.1… #> 7 AB12345 AB12345-BRA-11-id-8 2021-12-21 02:02:07 dcd C.1.1.1.3 3 C.1.1.1… #> 8 AB12345 AB12345-BRA-12-id-120 2020-10-01 01:42:29 dcd C.1.1.1.3 3 C.1.1.1… aag <- data.frame( NAMVAR = c(\"CQ01NAM\", \"CQ02NAM\", \"SMQ01NAM\", \"SMQ02NAM\"), REFNAME = c( \"D.2.1.5.3/A.1.1.1.1 aesi\", \"X.9.9.9.9/Y.8.8.8.8 aesi\", \"C.1.1.1.3/B.2.2.3.1 aesi\", \"C.1.1.1.3/B.3.3.3.3 aesi\" ), SCOPE = c(\"\", \"\", \"BROAD\", \"BROAD\"), stringsAsFactors = FALSE ) basket_name <- character(nrow(aag)) cq_pos <- grep(\"^(CQ).+NAM$\", aag$NAMVAR) smq_pos <- grep(\"^(SMQ).+NAM$\", aag$NAMVAR) basket_name[cq_pos] <- aag$REFNAME[cq_pos] basket_name[smq_pos] <- paste0( aag$REFNAME[smq_pos], \"(\", aag$SCOPE[smq_pos], \")\" ) aag_summary <- data.frame( basket = aag$NAMVAR, basket_name = basket_name, stringsAsFactors = TRUE ) result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary) all(levels(aag_summary$basket_name) %in% levels(result$SMQ)) #> [1] TRUE h_stack_by_baskets( df = adae, aag_summary = NULL, keys = c(\"STUDYID\", \"USUBJID\", \"AEDECOD\", \"ARM\"), baskets = \"SMQ01NAM\" ) #> # A tibble: 6 × 5 #> STUDYID USUBJID AEDECOD ARM SMQ #> #> 1 AB12345 AB12345-BRA-1-id-171 dcd C.1.1.1.3 B: Placebo C.1.1.1.3/B.2.2.3.… #> 2 AB12345 AB12345-BRA-1-id-23 dcd B.2.2.3.1 A: Drug X C.1.1.1.3/B.2.2.3.… #> 3 AB12345 AB12345-BRA-1-id-59 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.… #> 4 AB12345 AB12345-BRA-1-id-9 dcd C.1.1.1.3 C: Combination C.1.1.1.3/B.2.2.3.… #> 5 AB12345 AB12345-BRA-11-id-8 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.… #> 6 AB12345 AB12345-BRA-12-id-120 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.…"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"Helper functions used internally STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"","code":"h_step_window(x, control = control_step()) h_step_trt_effect(data, model, variables, x) h_step_survival_formula(variables, control = control_step()) h_step_survival_est( formula, data, variables, x, subset = rep(TRUE, nrow(data)), control = control_coxph() ) h_step_rsp_formula(variables, control = c(control_step(), control_logistic())) h_step_rsp_est( formula, data, variables, x, subset = rep(TRUE, nrow(data)), control = control_logistic() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"x (numeric) biomarker value(s) use (without NA). control (named list) output control_step(). data (data.frame) dataset containing variables summarize. model (coxph glm) regression model object. variables (named list string) list additional analysis variables. formula (formula) regression model formula. subset (logical) subset vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"h_step_window() returns list containing window-selection matrix sel interval information matrix interval. h_step_trt_effect() returns vector elements est se. h_step_survival_formula() returns model formula. h_step_survival_est() returns matrix number observations n, events, log hazard ratio estimates loghr, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x. h_step_rsp_formula() returns model formula. h_step_rsp_est() returns matrix number observations n, log odds ratio estimates logor, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"h_step_window(): Creates windows STEP, based control settings provided. h_step_trt_effect(): Calculates estimated treatment effect estimate linear predictor scale corresponding standard error STEP model fitted data given variables specification, single biomarker value x. works coxph glm models, .e. calculating log hazard ratio log odds ratio estimates. h_step_survival_formula(): Builds model formula used survival STEP calculations. h_step_survival_est(): Estimates model formula built based variables data given subset control parameters Cox regression. h_step_rsp_formula(): Builds model formula used response STEP calculations. h_step_rsp_est(): Estimates model formula built based variables data given subset control parameters logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"","code":"h_surv_to_coxreg_variables(variables, biomarker) h_coxreg_mult_cont_df(variables, data, control = control_coxreg()) h_tab_surv_one_biomarker( df, vars, time_unit, na_str = default_na_str(), .indent_mods = 0L, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (list) list parameters returned helper function control_coxreg(). df (data.frame) results single biomarker, part returned extract_survival_biomarkers() (needs couple columns added high-level function relative returned h_coxreg_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables() returns named list elements time, event, arm, covariates, strata. h_coxreg_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_surv_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables(): Helps converting \"survival\" function variable list \"Cox regression\" variable list. reason currently inconsistency variable names accepted extract_survival_subgroups() fit_coxreg_multivar(). h_coxreg_mult_cont_df(): Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables) optionally subgroups strata. h_tab_surv_one_biomarker(): Prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = FALSE) adtte_f <- adtte %>% filter(PARAMCD == \"OS\") %>% mutate( AVALU = as.character(AVALU), is_event = CNSR == 0 ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels # This is how the variable list is converted internally. h_surv_to_coxreg_variables( variables = list( tte = \"AVAL\", is_event = \"EVNT\", covariates = c(\"A\", \"B\"), strata = \"D\" ), biomarker = \"AGE\" ) #> $time #> [1] \"AVAL\" #> #> $event #> [1] \"EVNT\" #> #> $arm #> [1] \"AGE\" #> #> $covariates #> [1] \"A\" \"B\" #> #> $strata #> [1] \"D\" #> # For a single population, estimate separately the effects # of two biomarkers. df <- h_coxreg_mult_cont_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f ) df #> biomarker biomarker_label n_tot n_tot_events median hr #> 1 BMRKR1 Continuous Level Biomarker 1 200 141 753.5176 1.000189 #> 2 AGE Age 200 141 753.5176 1.008267 #> lcl ucl conf_level pval pval_label #> 1 0.9511092 1.051802 0.95 0.9941244 p-value (Wald) #> 2 0.9845155 1.032591 0.95 0.4984743 p-value (Wald) # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"REGION1\", strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f[NULL, ] ) #> biomarker biomarker_label n_tot n_tot_events median hr lcl ucl #> 1 BMRKR1 Continuous Level Biomarker 1 0 0 NA NA NA NA #> 2 AGE Age 0 0 NA NA NA NA #> conf_level pval pval_label #> 1 0.95 NA p-value (Wald) #> 2 0.95 NA p-value (Wald) # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_surv_one_biomarker( df1, vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"), time_unit = \"days\" ) #> Total n Total Events Median (days) Hazard Ratio 95% Wald CI p-value (Wald) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> All patients 200 141 753.5 1.00 (0.95, 1.05) 0.9941"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"Helper functions tabulate data frame statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"","code":"h_survtime_df(tte, is_event, arm) h_survtime_subgroups_df( variables, data, groups_lists = list(), label_all = \"All Patients\" ) h_coxph_df(tte, is_event, arm, strata_data = NULL, control = control_coxph()) h_coxph_subgroups_df( variables, data, groups_lists = list(), control = control_coxph(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"tte (numeric) vector time--event duration values. is_event (flag)TRUE event, FALSE time event censored. arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame, NULL) required stratified analysis performed. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing null hypothesis hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"h_survtime_df() returns data.frame columns arm, n, n_events, median. h_survtime_subgroups_df() returns data.frame columns arm, n, n_events, median, subgroup, var, var_label, row_type. h_coxph_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval pval_label. h_coxph_subgroups_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"h_survtime_df(): Helper prepare data frame median survival times arm. h_survtime_subgroups_df(): Summarizes median survival times arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_coxph_df(): Helper prepare data frame estimates treatment hazard ratio. h_coxph_subgroups_df(): Summarizes estimates treatment hazard ratio across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats) adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte) adtte_f <- adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\"), SEX %in% c(\"M\", \"F\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")), SEX = droplevels(SEX), is_event = CNSR == 0 ) labels <- c(\"ARM\" = adtte_labels[[\"ARM\"]], \"SEX\" = adtte_labels[[\"SEX\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels # Extract median survival time for one group. h_survtime_df( tte = adtte_f$AVAL, is_event = adtte_f$is_event, arm = adtte_f$ARM ) #> arm n n_events median #> 1 B: Placebo 73 57 727.8043 #> 2 A: Drug X 69 44 974.6402 # Extract median survival time for multiple groups. h_survtime_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) #> arm n n_events median subgroup var #> 1 B: Placebo 73 57 727.8043 All Patients ALL #> 2 A: Drug X 69 44 974.6402 All Patients ALL #> 3 B: Placebo 40 31 599.1772 F SEX #> 4 A: Drug X 38 24 1016.2982 F SEX #> 5 B: Placebo 33 26 888.4916 M SEX #> 6 A: Drug X 31 20 974.6402 M SEX #> 7 B: Placebo 24 21 735.4722 LOW BMRKR2 #> 8 A: Drug X 26 15 974.6402 LOW BMRKR2 #> 9 B: Placebo 23 14 731.8352 MEDIUM BMRKR2 #> 10 A: Drug X 26 17 964.2197 MEDIUM BMRKR2 #> 11 B: Placebo 26 22 654.8245 HIGH BMRKR2 #> 12 A: Drug X 17 12 1016.2982 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Define groupings for BMRKR2 levels. h_survtime_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n n_events median subgroup var #> 1 B: Placebo 73 57 727.8043 All Patients ALL #> 2 A: Drug X 69 44 974.6402 All Patients ALL #> 3 B: Placebo 40 31 599.1772 F SEX #> 4 A: Drug X 38 24 1016.2982 F SEX #> 5 B: Placebo 33 26 888.4916 M SEX #> 6 A: Drug X 31 20 974.6402 M SEX #> 7 B: Placebo 24 21 735.4722 low BMRKR2 #> 8 A: Drug X 26 15 974.6402 low BMRKR2 #> 9 B: Placebo 47 35 735.4722 low/medium BMRKR2 #> 10 A: Drug X 52 32 964.2197 low/medium BMRKR2 #> 11 B: Placebo 73 57 727.8043 low/medium/high BMRKR2 #> 12 A: Drug X 69 44 974.6402 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Extract hazard ratio for one group. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.057337 0.95 0.09049511 #> pval_label #> 1 p-value (log-rank) # Extract hazard ratio for one group with stratification factor. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.6646586 0.4399495 1.00414 0.95 0.05089188 #> pval_label #> 1 p-value (log-rank) # Extract hazard ratio for multiple groups. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> 2 78 55 0.5595391 0.3246658 0.9643271 0.95 0.03411759 #> 3 64 46 0.9102874 0.5032732 1.6464678 0.95 0.75582028 #> 4 50 36 0.7617717 0.3854349 1.5055617 0.95 0.43236030 #> 5 49 31 0.7651261 0.3641277 1.6077269 0.95 0.47860004 #> 6 43 34 0.6662356 0.3257413 1.3626456 0.95 0.26285846 #> pval_label subgroup var var_label row_type #> 1 p-value (log-rank) All Patients ALL All Patients content #> 2 p-value (log-rank) F SEX Sex analysis #> 3 p-value (log-rank) M SEX Sex analysis #> 4 p-value (log-rank) LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank) MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank) HIGH BMRKR2 Continuous Level Biomarker 2 analysis # Define groupings of BMRKR2 levels. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> 2 78 55 0.5595391 0.3246658 0.9643271 0.95 0.03411759 #> 3 64 46 0.9102874 0.5032732 1.6464678 0.95 0.75582028 #> 4 50 36 0.7617717 0.3854349 1.5055617 0.95 0.43236030 #> 5 99 67 0.7472958 0.4600419 1.2139136 0.95 0.23764314 #> 6 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> pval_label subgroup var var_label #> 1 p-value (log-rank) All Patients ALL All Patients #> 2 p-value (log-rank) F SEX Sex #> 3 p-value (log-rank) M SEX Sex #> 4 p-value (log-rank) low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank) low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> row_type #> 1 content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis # Extract hazard ratio for multiple groups with stratification factors. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.6126133 0.3913507 0.9589739 0.95 0.03086774 #> 2 78 55 0.3934024 0.2027682 0.7632630 0.95 0.00469167 #> 3 64 46 0.9501768 0.4730073 1.9087145 0.95 0.88580522 #> 4 50 36 0.7378635 0.3140465 1.7336363 0.95 0.48408079 #> 5 49 31 0.9408062 0.4172095 2.1215148 0.95 0.88305965 #> 6 43 34 0.5125617 0.2125140 1.2362459 0.95 0.13124382 #> pval_label subgroup var var_label row_type #> 1 p-value (log-rank) All Patients ALL All Patients content #> 2 p-value (log-rank) F SEX Sex analysis #> 3 p-value (log-rank) M SEX Sex analysis #> 4 p-value (log-rank) LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank) MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank) HIGH BMRKR2 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"Please see h_tab_surv_one_biomarker() h_tab_rsp_one_biomarker(), use function examples. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"","code":"h_tab_one_biomarker( df, afuns, colvars, na_str = default_na_str(), .indent_mods = 0L, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"df (data.frame) results single biomarker. afuns (named list function) analysis functions. colvars (named list) named list elements vars (variables tabulate) labels (labels). na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"rtables table object statistics columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"Create data.frame pairwise stratified unstratified Cox-PH analysis results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"","code":"h_tbl_coxph_pairwise( df, variables, ref_group_coxph = NULL, control_coxph_pw = control_coxph(), annot_coxph_ref_lbls = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strata (character NULL) variable names indicating stratification factors. ref_group_coxph (string NULL) level arm variable use reference group calculations annot_coxph table. NULL (default), uses first level arm variable. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. annot_coxph_ref_lbls (flag) whether reference group explicitly printed labels annot_coxph table. FALSE (default), comparison groups printed annot_coxph table labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"data.frame containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"","code":"library(dplyr) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) h_tbl_coxph_pairwise( df = adtte, variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\"), control_coxph_pw = control_coxph(conf_level = 0.9) ) #> HR 90% CI p-value (log-rank) #> B: Placebo 1.41 (1.01, 1.96) 0.0905 #> C: Combination 1.81 (1.24, 2.64) 0.0086"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for survival estimations — h_tbl_median_surv","title":"Helper function for survival estimations — h_tbl_median_surv","text":"Transform survival fit table groups rows characterized N, median confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for survival estimations — h_tbl_median_surv","text":"","code":"h_tbl_median_surv(fit_km, armval = \"All\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for survival estimations — h_tbl_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for survival estimations — h_tbl_median_surv","text":"summary table statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for survival estimations — h_tbl_median_surv","text":"","code":"library(dplyr) library(survival) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") fit <- survfit( formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = adtte ) h_tbl_median_surv(fit_km = fit) #> N Median 95% CI #> ARM A 69 974.6 (687, 1625) #> ARM B 73 727.8 (555.8, 1156) #> ARM C 58 632.3 (393, 1001)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"Helper function count number patients fraction patients according highest post-baseline lab grade variable .var, baseline lab grade variable baseline_var, direction interest specified direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"h_worsen_counter(df, id, .var, baseline_var, direction_var)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. .var (string) single variable name passed rtables requested statistics function. baseline_var (string) name baseline lab grade variable. direction_var (string) name direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\".","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" ) # `h_worsen_counter` h_worsen_counter( df %>% filter(PARAMCD == \"CRP\" & GRADDR == \"Low\"), id = \"USUBJID\", .var = \"ATOXGR\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\" ) #> $fraction #> $fraction$`1` #> num denom #> 25 180 #> #> $fraction$`2` #> num denom #> 15 186 #> #> $fraction$`3` #> num denom #> 18 191 #> #> $fraction$`4` #> num denom #> 17 196 #> #> $fraction$Any #> num denom #> 75 196 #> #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to calculate x-tick positions — h_xticks","title":"Helper function to calculate x-tick positions — h_xticks","text":"Calculate positions ticks x-axis. However, xticks already exists kept . based function ggplot2 relies , required graphic patient--risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to calculate x-tick positions — h_xticks","text":"","code":"h_xticks(data, xticks = NULL, max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to calculate x-tick positions — h_xticks","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to calculate x-tick positions — h_xticks","text":"vector positions use x-axis ticks ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to calculate x-tick positions — h_xticks","text":"","code":"library(dplyr) library(survival) data <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot() h_xticks(data) #> [1] 0 1000 2000 3000 4000 5000 h_xticks(data, xticks = seq(0, 3000, 500)) #> [1] 0 500 1000 1500 2000 2500 3000 h_xticks(data, xticks = 500) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500 h_xticks(data, xticks = 500, max_time = 6000) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 h_xticks(data, xticks = c(0, 500), max_time = 300) #> [1] 0 500 h_xticks(data, xticks = 500, max_time = 300) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/imputation_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply 1/3 or 1/2 imputation rule to data — imputation_rule","text":"","code":"imputation_rule( df, x_stats, stat, imp_rule, post = FALSE, avalcat_var = \"AVALCAT1\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/imputation_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply 1/3 or 1/2 imputation rule to data — imputation_rule","text":"df (data.frame) data set containing analysis variables. x_stats (named list) named list statistics, typically results s_summary(). stat (string) statistic return value/NA level according imputation rule applied. imp_rule (string) imputation rule setting. Set \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. post (flag) whether data corresponds post-dose time-point (defaults FALSE). parameter used imp_rule set \"1/3\". avalcat_var (string) name variable indicates whether row df corresponds analysis value category \"BLQ\", \"LTR\", \" $val #> max #> 0.9919061 #> #> $na_str #> [1] \"ND\" #> imputation_rule(df, x_stats, \"geom_mean\", \"1/3\") #> $val #> [1] NA #> #> $na_str #> [1] \"NE\" #> imputation_rule(df, x_stats, \"mean\", \"1/2\") #> $val #> [1] NA #> #> $na_str #> [1] \"ND\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence rate estimation — incidence_rate","title":"Incidence rate estimation — incidence_rate","text":"analyze function estimate_incidence_rate() creates layout element estimate event rate adjusted person-years risk, otherwise known incidence rate. primary analysis variable specified via vars person-years risk. addition variable, n_events variable number events observed (value 1 means event observed 0 means event observed) must also specified.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence rate estimation — incidence_rate","text":"","code":"estimate_incidence_rate( lyt, vars, n_events, id_var = \"USUBJID\", control = control_incidence_rate(), na_str = default_na_str(), nested = TRUE, summarize = FALSE, label_fmt = \"%s - %.labels\", ..., show_labels = \"hidden\", table_names = vars, .stats = c(\"person_years\", \"n_events\", \"rate\", \"rate_ci\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_incidence_rate( df, .var, n_events, is_event = lifecycle::deprecated(), id_var = \"USUBJID\", control = control_incidence_rate() ) a_incidence_rate( df, labelstr = \"\", .var, .df_row, n_events, id_var = \"USUBJID\", control = control_incidence_rate(), .stats = NULL, .formats = c(person_years = \"xx.x\", n_events = \"xx\", rate = \"xx.xx\", rate_ci = \"(xx.xx, xx.xx)\", n_unique = \"xx\", n_rate = \"xx (xx.x)\"), .labels = NULL, .indent_mods = NULL, na_str = default_na_str(), label_fmt = \"%s - %.labels\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence rate estimation — incidence_rate","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . n_events (string) name integer variable indicating whether event observed (1) (0). id_var (string) name variable used patient identifier \"n_unique\" included .stats. Defaults \"USUBJID\". control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level (proportion) confidence level estimated incidence rate. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) time unit desired output (person-years). na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. summarize (flag) whether function act analyze function (summarize = FALSE), summarize function (summarize = TRUE). Defaults FALSE. label_fmt (string) labels formatted row split occurs summarize = TRUE. string use \"%s\" represent row split levels, \"%.labels\" represent labels supplied .labels argument. Defaults \"%s - %.labels\". ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_incidence_rate\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. is_event (flag)TRUE event, FALSE time event censored. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .df_row (data.frame) data frame across columns given row split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence rate estimation — incidence_rate","text":"estimate_incidence_rate() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_incidence_rate() table layout. s_incidence_rate() returns following statistics: person_years: Total person-years risk. n_events: Total number events observed. rate: Estimated incidence rate. rate_ci: Confidence interval incidence rate. n_unique: Total number patients least one event observed. n_rate: Total number events observed & estimated incidence rate. a_incidence_rate() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Incidence rate estimation — incidence_rate","text":"estimate_incidence_rate(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_incidence_rate(): Statistics function estimates incidence rate associated confidence interval. a_incidence_rate(): Formatted analysis function used afun estimate_incidence_rate().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence rate estimation — incidence_rate","text":"","code":"df <- data.frame( USUBJID = as.character(seq(6)), CNSR = c(0, 1, 1, 0, 0, 0), AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4), ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\")), STRATA1 = factor(c(\"X\", \"Y\", \"Y\", \"X\", \"X\", \"Y\")) ) df$n_events <- 1 - df$CNSR basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% estimate_incidence_rate( vars = \"AVAL\", n_events = \"n_events\", control = control_incidence_rate( input_time_unit = \"month\", num_pt_year = 100 ) ) %>% build_table(df) #> A B #> (N=3) (N=3) #> ————————————————————————————————————————————————————————————————————— #> Total patient-years at risk 3.8 5.2 #> Number of adverse events observed 1 3 #> AE rate per 100 patient-years 26.20 57.23 #> 95% CI (-25.15, 77.55) (-7.53, 122.00) # summarize = TRUE basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\", child_labels = \"visible\") %>% estimate_incidence_rate( vars = \"AVAL\", n_events = \"n_events\", .stats = c(\"n_unique\", \"n_rate\"), summarize = TRUE, label_fmt = \"%.labels\" ) %>% build_table(df) #> A B #> (N=3) (N=3) #> ——————————————————————————————————————————————————————————————————————————————————————— #> X #> Total number of patients with at least one adverse event 1 2 #> Number of adverse events observed (AE rate per 100 patient-years) 1 (9.9) 2 (5.1) #> Y #> Total number of patients with at least one adverse event 0 1 #> Number of adverse events observed (AE rate per 100 patient-years) 0 (0.0) 1 (4.3) a_incidence_rate( df, .var = \"AVAL\", .df_row = df, n_events = \"n_events\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 person_years 108.7 0 #> 2 n_events 4 0 #> 3 rate 3.68 0 #> 4 rate_ci (0.07, 7.29) 0 #> 5 n_unique 4 0 #> 6 n_rate 4 (3.7) 0 #> row_label #> 1 Total patient-years at risk #> 2 Number of adverse events observed #> 3 AE rate per 100 patient-years #> 4 95% CI #> 5 Total number of patients with at least one adverse event #> 6 Number of adverse events observed (AE rate per 100 patient-years)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels or names of list elements — labels_or_names","title":"Labels or names of list elements — labels_or_names","text":"Internal helper function working nested statistic function results typically labels names can use.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels or names of list elements — labels_or_names","text":"","code":"labels_or_names(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels or names of list elements — labels_or_names","text":"x (list) list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels or names of list elements — labels_or_names","text":"character vector labels names list elements.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":null,"dir":"Reference","previous_headings":"","what":"Update labels according to control specifications — labels_use_control","title":"Update labels according to control specifications — labels_use_control","text":"Given list statistic labels list control parameters, updates labels relevant control specification. example, control element conf_level set 0.9, default label statistic mean_ci updated \"Mean 90% CI\". labels supplied via labels_custom updated regardless control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update labels according to control specifications — labels_use_control","text":"","code":"labels_use_control(labels_default, control, labels_custom = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update labels according to control specifications — labels_use_control","text":"labels_default (named character) named vector statistic labels modify according control specifications. Labels explicitly defined labels_custom affected. control (named list) list control parameters apply adjust default labels. labels_custom (named character) named vector labels customized user affected control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update labels according to control specifications — labels_use_control","text":"named character vector labels control specifications applied relevant labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update labels according to control specifications — labels_use_control","text":"","code":"control <- list(conf_level = 0.80, quantiles = c(0.1, 0.83), test_mean = 0.57) get_labels_from_stats(c(\"mean_ci\", \"quantiles\", \"mean_pval\")) %>% labels_use_control(control = control) #> mean_ci quantiles #> \"Mean 80% CI\" \"10% and 83%-ile\" #> mean_pval #> \"Mean p-value (H0: mean = 0.57)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic regression multivariate column layout function — logistic_regression_cols","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"Layout-creating function creates multivariate column layout summarizing logistic regression results. function wrapper rtables::split_cols_by_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"","code":"logistic_regression_cols(lyt, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"lyt (PreDataTableLayouts) layout analyses added . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"layout object suitable passing layouting functions. Adding function rtable layout split table columns corresponding statistics df, estimate, std_error, odds_ratio, ci, pvalue.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic regression summary table — logistic_summary_by_flag","title":"Logistic regression summary table — logistic_summary_by_flag","text":"Constructor content functions used summarize_logistic() summarize logistic regression results. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic regression summary table — logistic_summary_by_flag","text":"","code":"logistic_summary_by_flag( flag_var, na_str = default_na_str(), .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic regression summary table — logistic_summary_by_flag","text":"flag_var (string) variable name identifying row used content function. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic regression summary table — logistic_summary_by_flag","text":"content function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Make names without dots — make_names","title":"Make names without dots — make_names","text":"Make names without dots","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make names without dots — make_names","text":"","code":"make_names(nams)"},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make names without dots — make_names","text":"nams (character) vector original names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make names without dots — make_names","text":"character vector proper names, use dots contrast make.names().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of months to days — month2day","title":"Conversion of months to days — month2day","text":"Conversion months days. approximative calculation considers month average 30.4375 days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of months to days — month2day","text":"","code":"month2day(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of months to days — month2day","text":"x (numeric(1)) time months.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of months to days — month2day","text":"numeric vector time days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of months to days — month2day","text":"","code":"x <- c(13.25, 8.15, 1, 2.834) month2day(x) #> [1] 403.29688 248.06563 30.43750 86.25988"},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"Muffled car::Anova — muffled_car_anova","title":"Muffled car::Anova — muffled_car_anova","text":"Applied survival models, car::Anova() signal strata terms dropped model formula present, function deliberately muffles message.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muffled car::Anova — muffled_car_anova","text":"","code":"muffled_car_anova(mod, test_statistic)"},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muffled car::Anova — muffled_car_anova","text":"mod (coxph) Cox regression model fitted survival::coxph(). test_statistic (string) method used estimation p.values; wald (default) likelihood.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Muffled car::Anova — muffled_car_anova","text":"output car::Anova(), convergence message muffled.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of available (non-missing entries) in a vector — n_available","title":"Number of available (non-missing entries) in a vector — n_available","text":"Small utility function better readability.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of available (non-missing entries) in a vector — n_available","text":"","code":"n_available(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of available (non-missing entries) in a vector — n_available","text":"x (vector) vector count non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of available (non-missing entries) in a vector — n_available","text":"Number non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Odds ratio estimation — odds_ratio","title":"Odds ratio estimation — odds_ratio","text":"analyze function estimate_odds_ratio() creates layout element compare bivariate responses two groups estimating odds ratio confidence interval. primary analysis variable specified vars group variable. Additional variables can included analysis via variables argument, accepts arm, arm variable, strata, stratification variable. two arm levels present, can combined two groups using groups_list argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Odds ratio estimation — odds_ratio","text":"","code":"estimate_odds_ratio( lyt, vars, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = vars, .stats = \"or_ci\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_odds_ratio( df, .var, .ref_group, .in_ref_col, .df_row, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL ) a_odds_ratio( df, .var, .ref_group, .in_ref_col, .df_row, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Odds ratio estimation — odds_ratio","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_odds_ratio(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_odds_ratio\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. .df_row (data.frame) data frame across columns given row split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Odds ratio estimation — odds_ratio","text":"estimate_odds_ratio() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_odds_ratio() table layout. s_odds_ratio() returns named list statistics or_ci (containing est, lcl, ucl) n_tot. a_odds_ratio() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Odds ratio estimation — odds_ratio","text":"estimate_odds_ratio(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_odds_ratio(): Statistics function estimates odds ratio treatment control. variables list arm strata variable names must passed stratified analysis required. a_odds_ratio(): Formatted analysis function used afun estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Odds ratio estimation — odds_ratio","text":"function uses logistic regression unstratified analyses, conditional logistic regression stratified analyses. Wald confidence interval calculated specified confidence level. stratified analyses, currently implementation conditional likelihood confidence intervals, therefore likelihood confidence interval available option. vars contains responders non-responders odds ratio estimation possible returned values NA.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Odds ratio estimation — odds_ratio","text":"","code":"set.seed(12) dta <- data.frame( rsp = sample(c(TRUE, FALSE), 100, TRUE), grp = factor(rep(c(\"A\", \"B\"), each = 50), levels = c(\"A\", \"B\")), strata = factor(sample(c(\"C\", \"D\"), 100, TRUE)) ) l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% estimate_odds_ratio(vars = \"rsp\") build_table(l, df = dta) #> A B #> ———————————————————————————————————————————— #> Odds Ratio (95% CI) 0.85 (0.38 - 1.88) # Unstratified analysis. s_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta ) #> $or_ci #> est lcl ucl #> 0.8484848 0.3831831 1.8788053 #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #> #> $n_tot #> n_tot #> 100 #> attr(,\"label\") #> [1] \"Total n\" #> # Stratified analysis. s_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta, variables = list(arm = \"grp\", strata = \"strata\") ) #> $or_ci #> est lcl ucl #> 0.7689750 0.3424155 1.7269154 #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #> #> $n_tot #> n_tot #> 100 #> attr(,\"label\") #> [1] \"Total n\" #> a_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 or_ci 0.85 (0.38 - 1.88) 1 Odds Ratio (95% CI) #> 2 n_tot 100 0 Total n"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion difference estimation — prop_diff","title":"Proportion difference estimation — prop_diff","text":"analysis function estimate_proportion_diff() creates layout element estimate difference proportion responders within studied population. primary analysis variable, vars, logical variable indicating whether response occurred record. See method parameter options methods use constructing confidence interval proportion difference. stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion difference estimation — prop_diff","text":"","code":"estimate_proportion_diff( lyt, vars, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\", na_str = default_na_str(), nested = TRUE, ..., var_labels = vars, show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\" ) a_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion difference estimation — prop_diff","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_proportion_diff\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion difference estimation — prop_diff","text":"estimate_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion_diff() table layout. s_proportion_diff() returns named list elements diff diff_ci. a_proportion_diff() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion difference estimation — prop_diff","text":"estimate_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_proportion_diff(): Statistics function estimating difference terms responder proportion. a_proportion_diff(): Formatted analysis function used afun estimate_proportion_diff().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proportion difference estimation — prop_diff","text":"performing unstratified analysis, methods \"cmh\", \"strat_newcombe\", \"strat_newcombecc\" permitted.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion difference estimation — prop_diff","text":"","code":"## \"Mid\" case: 4/4 respond in group A, 1/2 respond in group B. nex <- 100 # Number of example rows dta <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE), \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE), \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE), stringsAsFactors = TRUE ) l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% estimate_proportion_diff( vars = \"rsp\", conf_level = 0.90, method = \"ha\" ) build_table(l, df = dta) #> A B #> —————————————————————————————————————————————————— #> Difference in Response rate (%) 12.0 #> 90% CI (Anderson-Hauck) (-5.4, 29.4) s_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, conf_level = 0.90, method = \"ha\" ) #> $diff #> [1] 12 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #> #> $diff_ci #> [1] -5.374519 29.374519 #> attr(,\"label\") #> [1] \"90% CI (Anderson-Hauck)\" #> # CMH example with strata s_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, variables = list(strata = c(\"f1\", \"f2\")), conf_level = 0.90, method = \"cmh\" ) #> $diff #> [1] 12.05847 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #> #> $diff_ci #> [1] -2.67057 26.78750 #> attr(,\"label\") #> [1] \"90% CI (CMH, without correction)\" #> a_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, conf_level = 0.90, method = \"ha\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 diff 12.0 0 Difference in Response rate (%) #> 2 diff_ci (-5.4, 29.4) 1 90% CI (Anderson-Hauck)"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Difference test for two proportions — prop_diff_test","title":"Difference test for two proportions — prop_diff_test","text":"analyze function test_proportion_diff() creates layout element test difference two proportions. primary analysis variable, vars, indicates whether response occurred record. See method parameter options methods use calculate p-value. Additionally, stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Difference test for two proportions — prop_diff_test","text":"","code":"test_proportion_diff( lyt, vars, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\"), na_str = default_na_str(), nested = TRUE, ..., var_labels = vars, show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_test_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") ) a_test_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Difference test for two proportions — prop_diff_test","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"test_proportion_diff\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Difference test for two proportions — prop_diff_test","text":"test_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_test_proportion_diff() table layout. s_test_proportion_diff() returns named list single item pval attribute label describing method used. p-value tests null hypothesis proportions two groups . a_test_proportion_diff() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Difference test for two proportions — prop_diff_test","text":"test_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_test_proportion_diff(): Statistics function tests difference two proportions. a_test_proportion_diff(): Formatted analysis function used afun test_proportion_diff().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Difference test for two proportions — prop_diff_test","text":"","code":"dta <- data.frame( rsp = sample(c(TRUE, FALSE), 100, TRUE), grp = factor(rep(c(\"A\", \"B\"), each = 50)), strata = factor(rep(c(\"V\", \"W\", \"X\", \"Y\", \"Z\"), each = 20)) ) # With `rtables` pipelines. l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% test_proportion_diff( vars = \"rsp\", method = \"cmh\", variables = list(strata = \"strata\") ) build_table(l, df = dta) #> A B #> ————————————————————————————————————————————————————— #> p-value (Cochran-Mantel-Haenszel Test) 1.0000"},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence table pruning — prune_occurrences","title":"Occurrence table pruning — prune_occurrences","text":"Family constructor condition functions flexibly prune occurrence tables. condition functions always return whether row result higher threshold. Since class CombinationFunction() can logically combined condition functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence table pruning — prune_occurrences","text":"","code":"keep_rows(row_condition) keep_content_rows(content_row_condition) has_count_in_cols(atleast, ...) has_count_in_any_col(atleast, ...) has_fraction_in_cols(atleast, ...) has_fraction_in_any_col(atleast, ...) has_fractions_difference(atleast, ...) has_counts_difference(atleast, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence table pruning — prune_occurrences","text":"row_condition (CombinationFunction) condition function works individual analysis rows flags whether kept pruned table. content_row_condition (CombinationFunction) condition function works individual first content rows leaf tables flags whether leaf tables kept pruned table. atleast (numeric(1)) threshold met order keep row. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence table pruning — prune_occurrences","text":"keep_rows() returns pruning function can used rtables::prune_table() prune rtables table. keep_content_rows() returns pruning function checks condition first content row leaf tables table. has_count_in_cols() returns condition function sums counts specified column. has_count_in_any_col() returns condition function compares counts specified columns threshold. has_fraction_in_cols() returns condition function sums counts specified column, computes fraction dividing total column counts. has_fraction_in_any_col() returns condition function looks fractions specified columns checks whether fulfill threshold. has_fractions_difference() returns condition function extracts fractions specified column, computes difference minimum maximum. has_counts_difference() returns condition function extracts counts specified column, computes difference minimum maximum.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence table pruning — prune_occurrences","text":"keep_rows(): Constructor creating pruning functions based row condition function. removes analysis rows (TableRow) pruned, .e., fulfill row condition. removes sub-tree children left. keep_content_rows(): Constructor creating pruning functions based condition (first) content row leaf tables. removes leaf tables first content row fulfill condition. check individual rows. proceeds recursively removing sub tree children left. has_count_in_cols(): Constructor creating condition functions total counts specified columns. has_count_in_any_col(): Constructor creating condition functions counts specified columns satisfying threshold. has_fraction_in_cols(): Constructor creating condition functions total fraction specified columns. has_fraction_in_any_col(): Constructor creating condition functions fraction specified columns. has_fractions_difference(): Constructor creating condition function checks difference fractions reported specified column. has_counts_difference(): Constructor creating condition function checks difference counts reported specified column.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence table pruning — prune_occurrences","text":"Since table specifications worded positively, name constructor condition functions positively, . However, note result keep_rows() says pruned, conform rtables::prune_table() interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence table pruning — prune_occurrences","text":"","code":"# \\donttest{ tab <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze_vars(\"COUNTRY\", .stats = \"count_fraction\") %>% build_table(DM) # } # \\donttest{ # `keep_rows` is_non_empty <- !CombinationFunction(all_zero_or_na) prune_table(tab, keep_rows(is_non_empty)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # `keep_content_rows` # \\donttest{ more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab)) prune_table(tab, keep_content_rows(more_than_twenty)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> CHE 0 0 0 #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> CAN 0 0 0 #> CHE 0 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> GBR 0 0 0 #> CAN 0 0 0 #> CHE 0 0 0 #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> RUS 0 0 0 #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> CAN 0 0 0 #> CHE 0 0 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> NGA 0 0 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> CHE 0 0 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> JPN 0 0 0 #> GBR 0 0 0 #> CAN 0 0 0 #> CHE 0 0 0 # } # \\donttest{ more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_count_in_any_col` any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(any_more_than_one)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_fraction_in_cols` more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> BLACK OR AFRICAN AMERICAN #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) # } # \\donttest{ # `has_fraction_in_any_col` any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(any_atleast_five_percent)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_fractions_difference` more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent_diff)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one_diff)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-implemented range() default S3 method for numerical objects — range_noinf","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"function returns c(NA, NA) instead c(-Inf, Inf) zero-length data without warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"","code":"range_noinf(x, na.rm = FALSE, finite = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"x (numeric) sequence numbers range computed. na.rm (flag) flag indicating NA omitted. finite (flag) flag indicating non-finite elements removed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"2-element vector class numeric.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Reapply variable labels — reapply_varlabels","title":"Reapply variable labels — reapply_varlabels","text":"helper function used tests.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reapply variable labels — reapply_varlabels","text":"","code":"reapply_varlabels(x, varlabels, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reapply variable labels — reapply_varlabels","text":"x (vector) vector elements needs new labels. varlabels (character) vector labels x. ... parameters added list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reapply variable labels — reapply_varlabels","text":"x variable labels reapplied.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"tabulate_rsp_biomarkers() function creates layout element tabulate estimated biomarker effects binary response endpoint across subgroups, returning statistics including response rate odds ratio population subgroup. table created df, list data frames returned extract_rsp_biomarkers(), statistics include specified via vars parameter. forest plot can created resulting table using g_forest() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"","code":"tabulate_rsp_biomarkers( df, vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"), na_str = default_na_str(), .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_rsp_biomarkers(). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"rtables table summarizing biomarker effects binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"contrast tabulate_rsp_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") df <- extract_rsp_biomarkers( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\" ), data = adrs_f ) # \\donttest{ ## Table with default columns. tabulate_rsp_biomarkers(df) #> Total n Responders Response (%) Odds Ratio 95% CI p-value (Wald) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age #> All Patients 200 164 82.0% 1.00 (0.95, 1.05) 0.8530 #> Continuous Level Biomarker 2 #> LOW 70 53 75.7% 0.93 (0.85, 1.01) 0.0845 #> MEDIUM 68 58 85.3% 0.99 (0.88, 1.11) 0.8190 #> HIGH 62 53 85.5% 1.06 (0.96, 1.18) 0.2419 #> Continuous Level Biomarker 1 #> All Patients 200 164 82.0% 0.98 (0.88, 1.08) 0.6353 #> Continuous Level Biomarker 2 #> LOW 70 53 75.7% 1.15 (0.95, 1.40) 0.1584 #> MEDIUM 68 58 85.3% 0.88 (0.73, 1.06) 0.1700 #> HIGH 62 53 85.5% 0.88 (0.72, 1.08) 0.2104 ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_rsp_biomarkers( df = df, vars = c(\"n_rsp\", \"ci\", \"n_tot\", \"prop\", \"or\") ) ## Finally produce the forest plot. g_forest(tab, xlim = c(0.7, 1.4)) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate binary response by subgroup — response_subgroups","title":"Tabulate binary response by subgroup — response_subgroups","text":"tabulate_rsp_subgroups() function creates layout element tabulate binary response subgroup, returning statistics including response rate odds ratio population subgroup. table created df, list data frames returned extract_rsp_subgroups(), statistics include specified via vars parameter. forest plot can created resulting table using g_forest() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate binary response by subgroup — response_subgroups","text":"","code":"tabulate_rsp_subgroups( lyt, df, vars = c(\"n_tot\", \"n\", \"prop\", \"or\", \"ci\"), groups_lists = list(), label_all = \"All Patients\", riskdiff = NULL, na_str = default_na_str(), .formats = c(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\") ) a_response_subgroups( .formats = list(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\", riskdiff = \"xx.x (xx.x - xx.x)\"), na_str = default_na_str() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate binary response by subgroup — response_subgroups","text":"lyt (PreDataTableLayouts) layout analyses added . df (list) list data frames containing analysis variables. List created using extract_rsp_subgroups(). vars (character) names statistics reported among: n: Total number observations per group. n_rsp: Number responders per group. prop: Proportion responders. n_tot: Total number observations. : Odds ratio. ci : Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, , ci required. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. riskdiff (list) risk (proportion) difference column added, list settings apply within column. See control_riskdiff() details. NULL, risk difference column added. riskdiff$arm_x riskdiff$arm_y NULL, first level df$prop$arm used arm_x second level arm_y. na_str (string) string used replace NA empty values output. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate binary response by subgroup — response_subgroups","text":"rtables table summarizing binary response subgroup. a_response_subgroups() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate binary response by subgroup — response_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate binary response by subgroup — response_subgroups","text":"tabulate_rsp_subgroups(): Table-creating function creates table summarizing binary response subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function used afun tabulate_rsp_subgroups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate binary response by subgroup — response_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") # Unstratified analysis. df <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) df #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.1804488 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.2669576 51.452218 0.95 F SEX #> 3 64 0.920000 0.2966470 2.853223 0.95 M SEX #> 4 50 3.553846 1.0047370 12.570277 0.95 LOW BMRKR2 #> 5 49 2.705882 0.5911718 12.385232 0.95 MEDIUM BMRKR2 #> 6 43 2.250000 0.3970298 12.750933 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Stratified analysis. df_strat <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = \"STRATA1\"), data = adrs_f ) df_strat #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.6343899 1.1537821 6.015009 0.95 All Patients ALL #> 2 78 9.5946605 2.0379337 45.171985 0.95 F SEX #> 3 64 0.8947158 0.2936803 2.725809 0.95 M SEX #> 4 50 3.5976656 1.0101319 12.813374 0.95 LOW BMRKR2 #> 5 49 2.6242168 0.5162572 13.339308 0.95 MEDIUM BMRKR2 #> 6 43 2.2816865 0.4116391 12.647228 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Grouping of the BMRKR2 levels. df_grouped <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) df_grouped #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 low BMRKR2 #> 8 A: Drug X 26 21 0.8076923 low BMRKR2 #> 9 B: Placebo 47 30 0.6382979 low/medium BMRKR2 #> 10 A: Drug X 52 44 0.8461538 low/medium BMRKR2 #> 11 B: Placebo 73 50 0.6849315 low/medium/high BMRKR2 #> 12 A: Drug X 69 59 0.8550725 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.180449 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.266958 51.452218 0.95 F SEX #> 3 64 0.920000 0.296647 2.853223 0.95 M SEX #> 4 50 3.553846 1.004737 12.570277 0.95 low BMRKR2 #> 5 99 3.116667 1.193409 8.139385 0.95 low/medium BMRKR2 #> 6 142 2.714000 1.180449 6.239827 0.95 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Table with default columns basic_table() %>% tabulate_rsp_subgroups(df) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24) #> Sex #> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45) #> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85) #> Continuous Level Biomarker 2 #> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57) #> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39) #> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75) # Table with selected columns basic_table() %>% tabulate_rsp_subgroups( df = df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\") ) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Responders Response (%) n Responders Response (%) Odds Ratio 95% CI #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 50 68.5% 69 59 85.5% 2.71 (1.18, 6.24) #> Sex #> F 78 40 25 62.5% 38 36 94.7% 10.80 (2.27, 51.45) #> M 64 33 25 75.8% 31 23 74.2% 0.92 (0.30, 2.85) #> Continuous Level Biomarker 2 #> LOW 50 24 13 54.2% 26 21 80.8% 3.55 (1.00, 12.57) #> MEDIUM 49 23 17 73.9% 26 23 88.5% 2.71 (0.59, 12.39) #> HIGH 43 26 20 76.9% 17 15 88.2% 2.25 (0.40, 12.75) # Table with risk difference column added basic_table() %>% tabulate_rsp_subgroups( df, riskdiff = control_riskdiff( arm_x = levels(df$prop$arm)[1], arm_y = levels(df$prop$arm)[2] ) ) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI Risk Difference (%) (95% CI) #> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24) -17.0 (-30.5 - -3.5) #> Sex #> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45) -32.2 (-48.8 - -15.6) #> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85) 1.6 (-19.7 - 22.8) #> Continuous Level Biomarker 2 #> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57) -26.6 (-51.6 - -1.6) #> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39) -14.5 (-36.3 - 7.2) #> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75) -11.3 (-33.6 - 11.0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert rtable objects to ggplot objects — rtable2gg","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"Given rtables::rtable() object, performs basic conversion ggplot2::ggplot() object built using functions ggplot2 package. table titles /footnotes ignored.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"","code":"rtable2gg(tbl, fontsize = 12, colwidths = NULL, lbl_col_padding = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"tbl (VTableTree)rtables table object. fontsize (numeric(1)) font size. colwidths (numeric NULL) vector column widths. element's position colwidths corresponds column tbl position. NULL, column widths calculated according maximum number characters per column. lbl_col_padding (numeric) additional padding use calculating spacing first (label) column second column tbl. colwidths specified, width first column becomes colwidths[1] + lbl_col_padding. Defaults 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"","code":"dta <- data.frame( ARM = rep(LETTERS[1:3], rep(6, 3)), AVISIT = rep(paste0(\"V\", 1:3), 6), AVAL = c(9:1, rep(NA, 9)) ) lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\") tbl <- build_table(lyt, df = dta) rtable2gg(tbl) rtable2gg(tbl, fontsize = 15, colwidths = c(2, 1, 1, 1))"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for accessing information from rtables — rtables_access","title":"Helper functions for accessing information from rtables — rtables_access","text":"couple functions help accessing data rtables objects. Currently work occurrence tables, defined count first element fraction second element cell.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for accessing information from rtables — rtables_access","text":"","code":"h_row_first_values(table_row, col_names = NULL, col_indices = NULL) h_row_counts(table_row, col_names = NULL, col_indices = NULL) h_row_fractions(table_row, col_names = NULL, col_indices = NULL) h_col_counts(table, col_names = NULL, col_indices = NULL) h_content_first_row(table) is_leaf_table(table) check_names_indices(table_row, col_names = NULL, col_indices = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for accessing information from rtables — rtables_access","text":"table_row (TableRow) analysis row occurrence table. col_names (character) names columns extract . col_indices (integer) indices columns extract . col_names provided, inferred names table_row. Note currently works well single column split. table (VTableNodeInfo) occurrence table row.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for accessing information from rtables — rtables_access","text":"h_row_first_values() returns vector numeric values. h_row_counts() returns vector numeric values. h_row_fractions() returns vector proportions. h_col_counts() returns vector column counts. h_content_first_row() returns row rtables table. is_leaf_table() returns logical value indicating whether current table leaf. check_names_indices returns column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for accessing information from rtables — rtables_access","text":"h_row_first_values(): Helper function extract first values content cell specified columns TableRow. Defaults columns. h_row_counts(): Helper function extracts row values checks convertible integers (integerish values). h_row_fractions(): Helper function extract fractions specified columns TableRow. specifically extracts second values content cell checks fraction. h_col_counts(): Helper function extract column counts specified columns table. h_content_first_row(): Helper function get first row content table current table. is_leaf_table(): Helper function says whether current table leaf tree. check_names_indices(): Internal helper function tests standard inputs column indices.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for accessing information from rtables — rtables_access","text":"","code":"tbl <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% analyze(\"AGE\", function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.x (xx.x)\"), \"n\" = length(x), \"frac\" = rcell(c(0.1, 0.1), format = \"xx (xx)\") ) }) %>% build_table(tern_ex_adsl) %>% prune_table() tree_row_elem <- collect_leaves(tbl[2, ])[[1]] result <- max(h_row_first_values(tree_row_elem)) result #> [1] 35.31214 # Row counts (integer values) # h_row_counts(tree_row_elem) # Fails because there are no integers # Using values with integers tree_row_elem <- collect_leaves(tbl[3, ])[[1]] result <- h_row_counts(tree_row_elem) # result # Row fractions tree_row_elem <- collect_leaves(tbl[4, ])[[1]] h_row_fractions(tree_row_elem) #> A: Drug X B: Placebo C: Combination #> 0.1 0.1 0.1"},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Cox model - summarized results — s_cox_multivariate","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"Analyses based multivariate Cox model usually performed Controlled Substance Reporting regulatory documents serve exploratory purposes (e.g., publication). practice, model usually includes main effects (without interaction terms). produces hazard ratio estimates covariates included model. analysis follows principles (e.g., stratified vs. unstratified analysis tie handling) usual Cox model analysis. Since usually pre-specified hypothesis testing analysis, p.values need interpreted caution. (Statistical Analysis Clinical Trials Data R, NEST's bookdown)","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"","code":"s_cox_multivariate( formula, data, conf_level = 0.95, pval_method = c(\"wald\", \"likelihood\"), ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"formula (formula) formula corresponding investigated survival::Surv() survival model including covariates. data (data.frame) data frame includes variable formula covariates. conf_level (proportion) confidence level hazard ratio interval estimations. Default 0.95. pval_method (string) method used estimation p-values, one \"wald\" (default) \"likelihood\". ... optional parameters passed survival::coxph(). Can include ties, character string specifying method tie handling, one exact (default), efron, breslow.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"list elements mod, msum, aov, coef_inter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"output limited single effect terms. Work ongoing estimation interaction terms scope defined Global Data Standards Repository (GDS_Standard_TLG_Specs_Tables_2.doc).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"","code":"library(dplyr) adtte <- tern_ex_adtte adtte_f <- subset(adtte, PARAMCD == \"OS\") # _f: filtered adtte_f <- filter( adtte_f, PARAMCD == \"OS\" & SEX %in% c(\"F\", \"M\") & RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\") ) adtte_f$SEX <- droplevels(adtte_f$SEX) adtte_f$RACE <- droplevels(adtte_f$RACE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert strings to NA — sas_na","title":"Convert strings to NA — sas_na","text":"SAS imports missing data empty strings strings whitespaces . helper function can used convert values NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert strings to NA — sas_na","text":"","code":"sas_na(x, empty = TRUE, whitespaces = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert strings to NA — sas_na","text":"x (factor character) values missing values substituted. empty (flag) TRUE, empty strings get replaced NA. whitespaces (flag) TRUE, strings made whitespaces get replaced NA.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert strings to NA — sas_na","text":"x \"\" /whitespace-values substituted NA, depending values empty whitespaces.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert strings to NA — sas_na","text":"","code":"sas_na(c(\"1\", \"\", \" \", \" \", \"b\")) #> [1] \"1\" NA NA NA \"b\" sas_na(factor(c(\"\", \" \", \"b\"))) #> [1] b #> Levels: b is.na(sas_na(c(\"1\", \"\", \" \", \" \", \"b\"))) #> [1] FALSE TRUE TRUE TRUE FALSE"},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence table sorting — score_occurrences","title":"Occurrence table sorting — score_occurrences","text":"Functions score occurrence table subtables rows can used sorting occurrence tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence table sorting — score_occurrences","text":"","code":"score_occurrences(table_row) score_occurrences_cols(...) score_occurrences_subtable(...) score_occurrences_cont_cols(...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence table sorting — score_occurrences","text":"table_row (TableRow) analysis row occurrence table. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence table sorting — score_occurrences","text":"score_occurrences() returns sum counts across columns table row. score_occurrences_cols() returns function sums counts across specified columns table row. score_occurrences_subtable() returns function sums counts subtable across specified columns. score_occurrences_cont_cols() returns function sums counts first content row specified columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence table sorting — score_occurrences","text":"score_occurrences(): Scoring function sums counts across columns. fail anything else counts used. score_occurrences_cols(): Scoring functions can produced constructor include specific columns scoring. See h_row_counts() information. score_occurrences_subtable(): Scoring functions produced constructor can used subtables: sum specified column counts subtable. useful available content row summing counts. score_occurrences_cont_cols(): Produces score function sorting table summing first content row specified columns. Note extending rtables::cont_n_onecol() rtables::cont_n_allcols().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence table sorting — score_occurrences","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% analyze_num_patients( vars = \"USUBJID\", .stats = c(\"unique\"), .labels = c(\"Total number of patients with at least one event\") ) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( \"Total number of patients with at least one event\", \"Total number of events\" ) ) %>% count_occurrences(vars = \"AEDECOD\") tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>% prune_table() tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_occurrences) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%) score_cols_a_and_b <- score_occurrences_cols(col_names = c(\"A: Drug X\", \"B: Placebo\")) # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted. # That would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_cols_a_and_b) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%) score_subtable_all <- score_occurrences_subtable(col_names = names(tbl)) # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That # would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\"), scorefun = score_subtable_all, decreasing = FALSE) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split columns by groups of levels — split_cols_by_groups","text":"","code":"split_cols_by_groups(lyt, var, groups_list = NULL, ref_group = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split columns by groups of levels — split_cols_by_groups","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. ref_group (data.frame vector) data corresponding reference group. ... additional arguments rtables::split_cols_by() order. instance, control formats (format), add joint column groups (incl_all).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split columns by groups of levels — split_cols_by_groups","text":"layout object suitable passing layouting functions. Adding function rtable layout add column split including given groups table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split columns by groups of levels — split_cols_by_groups","text":"","code":"# 1 - Basic use # Without group combination `split_cols_by_groups` is # equivalent to [rtables::split_cols_by()]. basic_table() %>% split_cols_by_groups(\"ARM\") %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> —————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 # Add a reference column. basic_table() %>% split_cols_by_groups(\"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff Mean\" = rcell(NULL)) } else { in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ——————————————————————————————————————————————————— #> Diff Mean 1.89 1.55 # 2 - Adding group specification # Manual preparation of the groups. groups <- list( \"Arms A+B\" = c(\"A: Drug X\", \"B: Placebo\"), \"Arms A+C\" = c(\"A: Drug X\", \"C: Combination\") ) # Use of split_cols_by_groups without reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> Arms A+B Arms A+C #> (N=227) (N=250) #> —————————————————————————— #> Mean 34.03 34.73 # Including differentiated output in the reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups_list = groups, ref_group = \"Arms A+B\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff. of Averages\" = rcell(NULL)) } else { in_rows(\"Diff. of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> Arms A+B Arms A+C #> ——————————————————————————————————————— #> Diff. of Averages 0.71 # 3 - Binary list dividing factor levels into reference and treatment # `combine_groups` defines reference and treatment. groups <- combine_groups( fct = DM$ARM, ref = c(\"A: Drug X\", \"B: Placebo\") ) groups #> $`A: Drug X/B: Placebo` #> [1] \"A: Drug X\" \"B: Placebo\" #> #> $`C: Combination` #> [1] \"C: Combination\" #> # Use group definition without reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups_list = groups) %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ———————————————————————————————————————————— #> Mean 34.03 34.57 # Use group definition with reference column (first item of groups). basic_table() %>% split_cols_by_groups(\"ARM\", groups, ref_group = names(groups)[1]) %>% add_colcounts() %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff Mean\" = rcell(NULL)) } else { in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ————————————————————————————————————————————————— #> Diff Mean 0.54"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Split text according to available text width — split_text_grob","title":"Split text according to available text width — split_text_grob","text":"Dynamically wrap text.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split text according to available text width — split_text_grob","text":"","code":"split_text_grob( text, x = grid::unit(0.5, \"npc\"), y = grid::unit(0.5, \"npc\"), width = grid::unit(1, \"npc\"), just = \"centre\", hjust = NULL, vjust = NULL, default.units = \"npc\", name = NULL, gp = grid::gpar(), vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split text according to available text width — split_text_grob","text":"text (string) text wrap. x numeric vector unit object specifying x-values. y numeric vector unit object specifying y-values. width (grid::unit) unit object specifying maximum width text. just justification text relative (x, y) location. two values, first value specifies horizontal justification second value specifies vertical justification. Possible string values : \"left\", \"right\", \"centre\", \"center\", \"bottom\", \"top\". numeric values, 0 means left (bottom) alignment 1 means right (top) alignment. hjust numeric vector specifying horizontal justification. specified, overrides just setting. vjust numeric vector specifying vertical justification. specified, overrides just setting. default.units string indicating default units use x y given numeric vectors. name character identifier. gp object class \"gpar\", typically output call function gpar. basically list graphical parameter settings. vp Grid viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split text according to available text width — split_text_grob","text":"text grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split text according to available text width — split_text_grob","text":"code taken R Graphics Paul Murell, 2nd edition","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack multiple grobs — stack_grobs","title":"Stack multiple grobs — stack_grobs","text":"Stack grobs new grob 1 column multiple rows layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack multiple grobs — stack_grobs","text":"","code":"stack_grobs( ..., grobs = list(...), padding = grid::unit(2, \"line\"), vp = NULL, gp = NULL, name = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack multiple grobs — stack_grobs","text":"... grobs. grobs (list grob) list grobs. padding (grid::unit) unit length 1, space grob. vp (viewport NULL) viewport() object (NULL). gp (gpar) gpar() object. name (string) character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack multiple grobs — stack_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stack multiple grobs — stack_grobs","text":"","code":"library(grid) g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid.newpage() grid.draw(stack_grobs(g1, g2, g3)) showViewport() grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = \"test\")) showViewport() grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence interval for mean — stat_mean_ci","title":"Confidence interval for mean — stat_mean_ci","text":"Convenient function calculating mean confidence interval. calculates arithmetic well geometric mean. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence interval for mean — stat_mean_ci","text":"","code":"stat_mean_ci( x, conf_level = 0.95, na.rm = TRUE, n_min = 2, gg_helper = TRUE, geom_mean = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence interval for mean — stat_mean_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric(1)) minimum number non-missing x estimate confidence interval mean. gg_helper (flag) whether output aligned use ggplots. geom_mean (flag) whether geometric mean calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence interval for mean — stat_mean_ci","text":"named vector values mean_ci_lwr mean_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence interval for mean — stat_mean_ci","text":"","code":"stat_mean_ci(sample(10), gg_helper = FALSE) #> mean_ci_lwr mean_ci_upr #> 3.334149 7.665851 p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) + ggplot2::geom_point() p + ggplot2::stat_summary( fun.data = stat_mean_ci, geom = \"errorbar\" ) p + ggplot2::stat_summary( fun.data = stat_mean_ci, fun.args = list(conf_level = 0.5), geom = \"errorbar\" ) p + ggplot2::stat_summary( fun.data = stat_mean_ci, fun.args = list(conf_level = 0.5, geom_mean = TRUE), geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":null,"dir":"Reference","previous_headings":"","what":"p-Value of the mean — stat_mean_pval","title":"p-Value of the mean — stat_mean_pval","text":"Convenient function calculating two-sided p-value mean.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"p-Value of the mean — stat_mean_pval","text":"","code":"stat_mean_pval(x, na.rm = TRUE, n_min = 2, test_mean = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"p-Value of the mean — stat_mean_pval","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric(1)) minimum number non-missing x estimate p-value mean. test_mean (numeric(1)) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"p-Value of the mean — stat_mean_pval","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"p-Value of the mean — stat_mean_pval","text":"","code":"stat_mean_pval(sample(10)) #> p_value #> 0.000278196 stat_mean_pval(rnorm(10), test_mean = 0.5) #> p_value #> 0.04235675"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence interval for median — stat_median_ci","title":"Confidence interval for median — stat_median_ci","text":"Convenient function calculating median confidence interval. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence interval for median — stat_median_ci","text":"","code":"stat_median_ci(x, conf_level = 0.95, na.rm = TRUE, gg_helper = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence interval for median — stat_median_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. gg_helper (flag) whether output aligned use ggplots.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence interval for median — stat_median_ci","text":"named vector values median_ci_lwr median_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence interval for median — stat_median_ci","text":"function adapted DescTools/versions/0.99.35/source","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence interval for median — stat_median_ci","text":"","code":"stat_median_ci(sample(10), gg_helper = FALSE) #> median_ci_lwr median_ci_upr #> 2 9 #> attr(,\"conf_level\") #> [1] 0.9785156 p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) + ggplot2::geom_point() p + ggplot2::stat_summary( fun.data = stat_median_ci, geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion difference and confidence interval — stat_propdiff_ci","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"Function calculating proportion (risk) difference confidence interval arm X (reference group) arm Y. Risk difference calculated subtracting cumulative incidence arm Y cumulative incidence arm X.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci( x, y, N_x, N_y, list_names = NULL, conf_level = 0.95, pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"x (list integer) list number occurrences arm X (reference group). y (list integer) list number occurrences arm Y. Must equal length x. N_x (numeric(1)) total number records arm X. N_y (numeric(1)) total number records arm Y. list_names (character) names variable/level corresponding pair proportions x y. Must equal length x y. conf_level (proportion) confidence level interval. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"List proportion differences CIs corresponding pair number occurrences x y. list element consists 3 statistics: proportion difference, CI lower bound, CI upper bound.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci( x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = \"x\", conf_level = 0.9 ) #> $x #> [1] 7.30000 -12.35184 26.95184 #> stat_propdiff_ci( x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE ) #> [[1]] #> [1] 0.0375000 -0.1060891 0.1810891 #> #> [[2]] #> [1] 0.07250000 -0.09220915 0.23720915 #> #> [[3]] #> [1] 0.0750000 -0.1231285 0.2731285 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"function wraps estimation stratified percentiles assume approximation large numbers. necessary case proportions strata unequal.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"","code":"strata_normal_quantile(vars, weights, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"vars (character) variable names primary analysis variable iterated . weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"Stratified quantile.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"","code":"strata_data <- table(data.frame( \"f1\" = sample(c(TRUE, FALSE), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE )) ns <- colSums(strata_data) ests <- strata_data[\"TRUE\", ] / ns vars <- ests * (1 - ests) / ns weights <- rep(1 / length(ns), length(ns)) strata_normal_quantile(vars, weights, 0.95) #> [1] 1.14181"},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicate study arm variable in formula — study_arm","title":"Indicate study arm variable in formula — study_arm","text":"use study_arm indicate study arm variable tern formulas.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicate study arm variable in formula — study_arm","text":"","code":"study_arm(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicate study arm variable in formula — study_arm","text":"x arm information","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicate study arm variable in formula — study_arm","text":"x","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"analyze function summarize_ancova() creates layout element summarize ANCOVA results. function can used analyze multiple endpoints /multiple timepoints within response variable(s) specified vars. Additional variables analysis, namely arm (grouping) variable covariate variables, can defined via variables argument. See details specify variables. interaction term can implemented model needed. interaction variable interact arm variable specified via interaction_term parameter, specific value interaction_term extract ANCOVA results via interaction_y parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"","code":"summarize_ancova( lyt, vars, variables, conf_level, interaction_y = FALSE, interaction_item = NULL, var_labels, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"visible\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_ancova( df, .var, .df_row, variables, .ref_group, .in_ref_col, conf_level, interaction_y = FALSE, interaction_item = NULL ) a_ancova( df, .var, .df_row, variables, .ref_group, .in_ref_col, conf_level, interaction_y = FALSE, interaction_item = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". conf_level (proportion) confidence level interval. interaction_y (string flag) selected item inside interaction_item variable used select specific ANCOVA results. interaction needed, default option FALSE. interaction_item (string NULL) name variable interactions arm. interaction needed, default option NULL. var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"summarize_ancova\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"summarize_ancova() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_ancova() table layout. s_ancova() returns named list 5 statistics: n: Count complete sample size group. lsmean: Estimated marginal means group. lsmean_diff: Difference estimated marginal means comparison reference group. working reference group, empty. lsmean_diff_ci: Confidence level difference estimated marginal means comparison reference group. pval: p-value (adjusted multiple comparisons). a_ancova() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"summarize_ancova(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_ancova(): Statistics function produces named list results investigated linear model. a_ancova(): Formatted analysis function used afun summarize_ancova().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"","code":"basic_table() %>% split_cols_by(\"Species\", ref_group = \"setosa\") %>% add_colcounts() %>% summarize_ancova( vars = \"Petal.Length\", variables = list(arm = \"Species\", covariates = NULL), table_names = \"unadj\", conf_level = 0.95, var_labels = \"Unadjusted comparison\", .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\") ) %>% summarize_ancova( vars = \"Petal.Length\", variables = list(arm = \"Species\", covariates = c(\"Sepal.Length\", \"Sepal.Width\")), table_names = \"adj\", conf_level = 0.95, var_labels = \"Adjusted comparison (covariates: Sepal.Length and Sepal.Width)\" ) %>% build_table(iris) #> setosa versicolor virginica #> (N=50) (N=50) (N=50) #> ————————————————————————————————————————————————————————————————————————————————————————————————————— #> Unadjusted comparison #> n 50 50 50 #> Mean 1.46 4.26 5.55 #> Difference in Means 2.80 4.09 #> 95% CI (2.63, 2.97) (3.92, 4.26) #> p-value <0.0001 <0.0001 #> Adjusted comparison (covariates: Sepal.Length and Sepal.Width) #> n 50 50 50 #> Adjusted Mean 2.02 4.19 5.07 #> Difference in Adjusted Means 2.17 3.05 #> 95% CI (1.96, 2.38) (2.81, 3.29) #> p-value <0.0001 <0.0001"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize change from baseline values or absolute baseline values — summarize_change","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"analyze function summarize_change() creates layout element summarize change baseline absolute baseline values. primary analysis variable vars indicates numerical change baseline results. Required secondary analysis variables value baseline_flag can supplied function via variables argument. value element name analysis value variable, baseline_flag element name flag variable indicates whether records contain baseline values. Depending baseline flag given, either absolute baseline values (baseline) change baseline values (post-baseline) summarized.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"","code":"summarize_change( lyt, vars, variables, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = c(\"n\", \"mean_sd\", \"median\", \"range\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_change_from_baseline(df, .var, variables, na.rm = TRUE, ...) a_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric) see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. na.rm (flag) whether NA values removed x prior analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"summarize_change() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_change_from_baseline() table layout. s_change_from_baseline() returns values returned s_summary.numeric(). a_change_from_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"summarize_change(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_change_from_baseline(): Statistics function summarizes baseline post-baseline visits. a_change_from_baseline(): Formatted analysis function used afun summarize_change().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"used split visits layout, data subset contains either baseline post-baseline data. data df must either baseline post-baseline visits. Otherwise error thrown.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"","code":"library(dplyr) ## Fabricate dataset dta_test <- data.frame( USUBJID = rep(1:6, each = 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)) ) %>% mutate(ABLFLL = AVISIT == \"V1\") %>% group_by(USUBJID) %>% mutate( BLVAL = AVAL[ABLFLL], CHG = AVAL - BLVAL ) %>% ungroup() results <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% summarize_change(\"CHG\", variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\")) %>% build_table(dta_test) results #> A B C #> ——————————————————————————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean (SD) 7.50 (2.12) 3.00 () #> Median 7.50 3.00 #> Min - Max 6.00 - 9.00 3.00 - 3.00 #> V2 #> n 2 1 0 #> Mean (SD) -1.00 (0.00) -1.00 () #> Median -1.00 -1.00 #> Min - Max -1.00 - -1.00 -1.00 - -1.00 #> V3 #> n 2 1 0 #> Mean (SD) -2.00 (0.00) -2.00 () #> Median -2.00 -2.00 #> Min - Max -2.00 - -2.00 -2.00 - -2.00 "},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize variables in columns — summarize_colvars","title":"Summarize variables in columns — summarize_colvars","text":"analyze function summarize_colvars() uses statistics function s_summary() analyze variables arranged columns. variables analyze specified table layout via column splits (see split_cols_by() split_cols_by_multivar()) prior using summarize_colvars(). function minimal wrapper rtables::analyze_colvars(), function typically used apply different analysis methods rows column variable. use analysis methods column labels, please refer analyze_vars_in_cols() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize variables in columns — summarize_colvars","text":"","code":"summarize_colvars( lyt, ..., na_str = default_na_str(), .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"), .formats = NULL, .labels = NULL, .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize variables in columns — summarize_colvars","text":"lyt (PreDataTableLayouts) layout analyses added . ... arguments passed s_summary(). na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize variables in columns — summarize_colvars","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize variables in columns — summarize_colvars","text":"","code":"dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)), CHG = c(1:9, rep(NA, 9)) ) ## Default output within a `rtables` pipeline. basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% summarize_colvars() %>% build_table(dta_test) #> A B C #> AVAL CHG AVAL CHG AVAL CHG #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> V1 #> n 2 2 1 1 0 0 #> Mean (SD) 7.5 (2.1) 2.5 (2.1) 3.0 () 7.0 () #> Median 7.5 2.5 3.0 7.0 #> Min - Max 6.0 - 9.0 1.0 - 4.0 3.0 - 3.0 7.0 - 7.0 #> V2 #> n 2 2 1 1 0 0 #> Mean (SD) 6.5 (2.1) 3.5 (2.1) 2.0 () 8.0 () #> Median 6.5 3.5 2.0 8.0 #> Min - Max 5.0 - 8.0 2.0 - 5.0 2.0 - 2.0 8.0 - 8.0 #> V3 #> n 2 2 1 1 0 0 #> Mean (SD) 5.5 (2.1) 4.5 (2.1) 1.0 () 9.0 () #> Median 5.5 4.5 1.0 9.0 #> Min - Max 4.0 - 7.0 3.0 - 6.0 1.0 - 1.0 9.0 - 9.0
r adsl$SEX[adsl$SEX == "M"] <- NA adsl <- df_explicit_na(adsl, na_level = "Missing Values") -result <- basic_table(show_colcounts = TRUE) %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% +result <- basic_table(show_colcounts = TRUE) %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) @@ -283,14 +253,14 @@ Missing Values in Numeric Variables "Sex" ) -result <- basic_table(show_colcounts = TRUE) %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% +result <- basic_table(show_colcounts = TRUE) %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) diff --git a/main/articles/tables.html b/main/articles/tables.html index 5632e2dd5a..faf9101a1c 100644 --- a/main/articles/tables.html +++ b/main/articles/tables.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -122,7 +92,7 @@ analyses from clinical trials in R. The core functionality for tabulation is built on the more general purpose rtables package. New users should first begin by reading the “Introduction -to tern” and “Introduction +to tern” and “Introduction to rtables” vignettes. The packages used in this vignette are: @@ -143,10 +113,10 @@ rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analyze functions are wrappers around -rtables::analyze function, they offer various methods +rtables::analyze function, they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analyze functions are @@ -173,26 +143,26 @@ Internals of tern Anal rcell type formatting themselves. formatted analysis functions a_*. These have the same arguments as the corresponding statistics functions, and can be further -customized by calling rtables::make_afun() on them. They -are used as afun in rtables::analyze(). +customized by calling rtables::make_afun() on them. They +are used as afun in rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions are used in combination with the rtables layout functions, in the pipeline which creates the table. They are the last element of the chain. -We will use the native rtables::analyze function with +We will use the native rtables::analyze function with the tern formatted analysis functions as a afun parameter. -l <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = a_summary) +l <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = a_summary) -build_table(l, df = adrs) -The rtables::make_afun function is helpful when somebody +build_table(l, df = adrs) +The rtables::make_afun function is helpful when somebody wants to attach some format to the formatted analysis function. -afun <- make_afun( +afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = "xx."), @@ -200,12 +170,12 @@ Internals of tern Anal .indent_mods = c(median = 1L) ) -l2 <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = afun) +l2 <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = afun) -build_table(l2, df = adrs) +build_table(l2, df = adrs) @@ -260,15 +230,15 @@ Demographic Tablevars <- c("AGE", "SEX") var_labels <- c("Age (yr)", "Sex") -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— @@ -295,15 +265,15 @@ Demographic Table# Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c("M", "F", "U", "UNDIFFERENTIATED")) -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% - build_table(adsl) + build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— @@ -323,8 +293,8 @@ Demographic Tableanalyze(), analyze_colvars() and -summarize_row_groups() and provide options for easy +analyze(), analyze_colvars() and +summarize_row_groups() and provide options for easy formatting and analysis modifications. To customize the display for the demographics table, we can do so via the arguments in analyze_vars(). Most layout creating @@ -343,17 +313,17 @@ Demographic Table # Select statistics and modify default formats. -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels, .stats = c("n", "mean_sd", "count"), .formats = c(mean_sd = "xx.xx (xx.xx)") ) %>% - build_table(adsl) + build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ———————————————————————————————————————————————————————————————————————————————— @@ -371,16 +341,16 @@ Demographic Table -lyt <- basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients(): -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred. a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -236,17 +206,17 @@ ArgumentsValue count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout. s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag. -a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE. a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,16 +191,16 @@ ArgumentsValue count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout. s_count_values() returns output of s_summary() for specified values of a non-numeric variable. -a_count_values() returns the corresponding list with formatted rtables::CellValue(). +a_count_values() returns the corresponding list with formatted rtables::CellValue(). Functions count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_values(): S3 generic function to count values. s_count_values(character): Method for character class. s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -212,7 +182,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -238,10 +208,10 @@ ArgumentsValue summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout. s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s). -a_coxreg() returns formatted rtables::CellValue(). +a_coxreg() returns formatted rtables::CellValue(). Details @@ -257,11 +227,11 @@ Functionsrtables::analyze_colvars() and rtables::summarize_row_groups(). +is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups(). s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list. -a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg(). +a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg(). See also diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html index 6124a8f4c1..b15b4c925c 100644 --- a/main/reference/cox_regression_inter.html +++ b/main/reference/cox_regression_inter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -210,7 +180,7 @@ Note Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table(). +variables before passing the dataset to rtables::build_table(). diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html index 976e7c3dcc..c3759b2a0d 100644 --- a/main/reference/cut_quantile_bins.html +++ b/main/reference/cut_quantile_bins.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html index 5b9e065ed8..9cba629d12 100644 --- a/main/reference/d_count_abnormal_by_baseline.html +++ b/main/reference/d_count_abnormal_by_baseline.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html index 8f5e09613a..ed30a89198 100644 --- a/main/reference/d_count_cumulative.html +++ b/main/reference/d_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_missed_doses.html b/main/reference/d_count_missed_doses.html index 3983179580..88d7cb115f 100644 --- a/main/reference/d_count_missed_doses.html +++ b/main/reference/d_count_missed_doses.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_onco_rsp_label.html b/main/reference/d_onco_rsp_label.html index 6ad11d55b2..36eb740b33 100644 --- a/main/reference/d_onco_rsp_label.html +++ b/main/reference/d_onco_rsp_label.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html index 50dd3d760f..911054124b 100644 --- a/main/reference/d_pkparam.html +++ b/main/reference/d_pkparam.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html index 45eecb0d3c..8a8f84d672 100644 --- a/main/reference/d_proportion.html +++ b/main/reference/d_proportion.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html index 513d2d47ae..fc4b954e11 100644 --- a/main/reference/d_proportion_diff.html +++ b/main/reference/d_proportion_diff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html index 953122b65f..cdcbc5e18c 100644 --- a/main/reference/d_rsp_subgroups_colvars.html +++ b/main/reference/d_rsp_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html index 8974168b2e..ad93487971 100644 --- a/main/reference/d_survival_subgroups_colvars.html +++ b/main/reference/d_survival_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html index f32963f964..3c31b90c80 100644 --- a/main/reference/d_test_proportion_diff.html +++ b/main/reference/d_test_proportion_diff.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/day2month.html b/main/reference/day2month.html index 4cdf9eb8d3..00a2059911 100644 --- a/main/reference/day2month.html +++ b/main/reference/day2month.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob.html b/main/reference/decorate_grob.html index 05bab36561..e0ee545cc9 100644 --- a/main/reference/decorate_grob.html +++ b/main/reference/decorate_grob.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_factory.html b/main/reference/decorate_grob_factory.html index bf15634600..c128f2d675 100644 --- a/main/reference/decorate_grob_factory.html +++ b/main/reference/decorate_grob_factory.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_set.html b/main/reference/decorate_grob_set.html index 5193dd3c1b..0de85e9334 100644 --- a/main/reference/decorate_grob_set.html +++ b/main/reference/decorate_grob_set.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_na_str.html b/main/reference/default_na_str.html index 53dc58920e..406deecf68 100644 --- a/main/reference/default_na_str.html +++ b/main/reference/default_na_str.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_stats_formats_labels.html b/main/reference/default_stats_formats_labels.html index 9969f93d9f..f98b60bff5 100644 --- a/main/reference/default_stats_formats_labels.html +++ b/main/reference/default_stats_formats_labels.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,7 +193,7 @@ Note These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings. Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables"). +return a string. This is well documented in vignette("custom_appearance", package = "rtables"). See also diff --git a/main/reference/desctools_binom.html b/main/reference/desctools_binom.html index 4b01634fe0..6be9e52e5c 100644 --- a/main/reference/desctools_binom.html +++ b/main/reference/desctools_binom.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df2gg.html b/main/reference/df2gg.html index d2a0c06ec1..3459218bc2 100644 --- a/main/reference/df2gg.html +++ b/main/reference/df2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df_explicit_na.html b/main/reference/df_explicit_na.html index 7239b2d0e5..3e4a639463 100644 --- a/main/reference/df_explicit_na.html +++ b/main/reference/df_explicit_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/dot-is_equal_float.html b/main/reference/dot-is_equal_float.html index 0e13761934..6c1e53f091 100644 --- a/main/reference/dot-is_equal_float.html +++ b/main/reference/dot-is_equal_float.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/draw_grob.html b/main/reference/draw_grob.html index 451b8e7fd6..f6cdcb0a01 100644 --- a/main/reference/draw_grob.html +++ b/main/reference/draw_grob.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/empty_vector_if_na.html b/main/reference/empty_vector_if_na.html index 7607d61187..452a306105 100644 --- a/main/reference/empty_vector_if_na.html +++ b/main/reference/empty_vector_if_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_coef.html b/main/reference/estimate_coef.html index 2bc5525f87..ce5485013a 100644 --- a/main/reference/estimate_coef.html +++ b/main/reference/estimate_coef.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_multinomial_rsp.html b/main/reference/estimate_multinomial_rsp.html index c917481057..4960eb095e 100644 --- a/main/reference/estimate_multinomial_rsp.html +++ b/main/reference/estimate_multinomial_rsp.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -177,17 +147,17 @@ ArgumentsValue estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout. s_length_proportion() returns statistics from s_proportion(). -a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::analyze() and +rtables::summarize_row_groups(). s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion(). a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -244,17 +214,17 @@ ArgumentsValue estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout. s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable. -a_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion(): Statistics function estimating a proportion along with its confidence interval. a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html index a19da4827a..75158ed039 100644 --- a/main/reference/explicit_na.html +++ b/main/reference/explicit_na.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html index 890e00c7b2..7f4ea02325 100644 --- a/main/reference/extract_by_name.html +++ b/main/reference/extract_by_name.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_biomarkers.html b/main/reference/extract_rsp_biomarkers.html index 47f4183173..22d0b045c8 100644 --- a/main/reference/extract_rsp_biomarkers.html +++ b/main/reference/extract_rsp_biomarkers.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_subgroups.html b/main/reference/extract_rsp_subgroups.html index 10e7fd68dd..bca8059217 100644 --- a/main/reference/extract_rsp_subgroups.html +++ b/main/reference/extract_rsp_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_biomarkers.html b/main/reference/extract_survival_biomarkers.html index 4756961aa7..353e5cea60 100644 --- a/main/reference/extract_survival_biomarkers.html +++ b/main/reference/extract_survival_biomarkers.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_subgroups.html b/main/reference/extract_survival_subgroups.html index 825bc1eedd..fc3a8da8f2 100644 --- a/main/reference/extract_survival_subgroups.html +++ b/main/reference/extract_survival_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extreme_format.html b/main/reference/extreme_format.html index cfb5075e63..c363e0dcb0 100644 --- a/main/reference/extreme_format.html +++ b/main/reference/extreme_format.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html index a2fa7874b0..e59e2a03ae 100644 --- a/main/reference/f_conf_level.html +++ b/main/reference/f_conf_level.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html index 1c4fea37df..5d7ab493ce 100644 --- a/main/reference/f_pval.html +++ b/main/reference/f_pval.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html index 83ab24eb82..f332240368 100644 --- a/main/reference/fct_collapse_only.html +++ b/main/reference/fct_collapse_only.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html index d7fb3b6c64..4cac1ccba5 100644 --- a/main/reference/fct_discard.html +++ b/main/reference/fct_discard.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_explicit_na_if.html b/main/reference/fct_explicit_na_if.html index 3bcaf94176..6848982dde 100644 --- a/main/reference/fct_explicit_na_if.html +++ b/main/reference/fct_explicit_na_if.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_coxreg.html b/main/reference/fit_coxreg.html index 14c5d95f6b..5c2bfc26a0 100644 --- a/main/reference/fit_coxreg.html +++ b/main/reference/fit_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html index 0f364c07c5..4e6119b249 100644 --- a/main/reference/fit_logistic.html +++ b/main/reference/fit_logistic.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html index f5d42bfe7c..5f64d2f786 100644 --- a/main/reference/fit_rsp_step.html +++ b/main/reference/fit_rsp_step.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html index b7da54c090..ca68ad9a04 100644 --- a/main/reference/fit_survival_step.html +++ b/main/reference/fit_survival_step.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html index 2e578ce576..964ab27f65 100644 --- a/main/reference/forest_viewport.html +++ b/main/reference/forest_viewport.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html index 66cff1543b..f5b5ca5633 100644 --- a/main/reference/format_auto.html +++ b/main/reference/format_auto.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -106,7 +76,7 @@ Argumentsdt_var (numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered. diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html index 7c89de6365..4559bb997c 100644 --- a/main/reference/format_count_fraction.html +++ b/main/reference/format_count_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html index fb67db8371..ae6e985489 100644 --- a/main/reference/format_count_fraction_fixed_dp.html +++ b/main/reference/format_count_fraction_fixed_dp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html index 9c0e167253..ce2f2e82e2 100644 --- a/main/reference/format_count_fraction_lt10.html +++ b/main/reference/format_count_fraction_lt10.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html index c12a059e98..6fdb3dd26f 100644 --- a/main/reference/format_extreme_values.html +++ b/main/reference/format_extreme_values.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html index dea1549bfe..c60b369a7d 100644 --- a/main/reference/format_extreme_values_ci.html +++ b/main/reference/format_extreme_values_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html index 8d4274cffb..2c2145230d 100644 --- a/main/reference/format_fraction.html +++ b/main/reference/format_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html index 297df429d5..3c5ba714ad 100644 --- a/main/reference/format_fraction_fixed_dp.html +++ b/main/reference/format_fraction_fixed_dp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_threshold.html b/main/reference/format_fraction_threshold.html index fc7c8ddea7..def9b6a40e 100644 --- a/main/reference/format_fraction_threshold.html +++ b/main/reference/format_fraction_threshold.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_sigfig.html b/main/reference/format_sigfig.html index 02a2b8591e..692c3a6abc 100644 --- a/main/reference/format_sigfig.html +++ b/main/reference/format_sigfig.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_xx.html b/main/reference/format_xx.html index 0ed5c9a3d8..18bbe09129 100644 --- a/main/reference/format_xx.html +++ b/main/reference/format_xx.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html index d325263a97..13af4631b1 100644 --- a/main/reference/formatting_functions.html +++ b/main/reference/formatting_functions.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_forest.html b/main/reference/g_forest.html index 3c11db2339..90308fb9b9 100644 --- a/main/reference/g_forest.html +++ b/main/reference/g_forest.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,7 +191,7 @@ Value Details - Given a rtables::rtable() object with at least one column with a single value and one column with 2 + Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side. diff --git a/main/reference/g_ipp.html b/main/reference/g_ipp.html index a0fe570f3e..187580ab44 100644 --- a/main/reference/g_ipp.html +++ b/main/reference/g_ipp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_km.html b/main/reference/g_km.html index cdfc1967d3..f352196584 100644 --- a/main/reference/g_km.html +++ b/main/reference/g_km.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html index 19a73c87f9..f690b652d5 100644 --- a/main/reference/g_lineplot.html +++ b/main/reference/g_lineplot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_step.html b/main/reference/g_step.html index c610853a23..eb4f82b05a 100644 --- a/main/reference/g_step.html +++ b/main/reference/g_step.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html index cfd51f294f..de2d4f49d4 100644 --- a/main/reference/g_waterfall.html +++ b/main/reference/g_waterfall.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_covariates.html b/main/reference/get_covariates.html index f17bf37bfe..ed76800bad 100644 --- a/main/reference/get_covariates.html +++ b/main/reference/get_covariates.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html index 6214ffa6c9..6215eb9b9d 100644 --- a/main/reference/get_smooths.html +++ b/main/reference/get_smooths.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html index 6e3a623ff0..b5b046e9bb 100644 --- a/main/reference/groups_list_to_df.html +++ b/main/reference/groups_list_to_df.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -76,7 +46,7 @@ - This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). + This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). diff --git a/main/reference/h_adlb_abnormal_by_worst_grade.html b/main/reference/h_adlb_abnormal_by_worst_grade.html index 924b3126fd..124a0f60f6 100644 --- a/main/reference/h_adlb_abnormal_by_worst_grade.html +++ b/main/reference/h_adlb_abnormal_by_worst_grade.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html index c86690c603..a1da8de34a 100644 --- a/main/reference/h_adlb_worsen.html +++ b/main/reference/h_adlb_worsen.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html index 5fc0bf7381..b1b901e4e0 100644 --- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html +++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ancova.html b/main/reference/h_ancova.html index 27bf03975f..59f11617c8 100644 --- a/main/reference/h_ancova.html +++ b/main/reference/h_ancova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_append_grade_groups.html b/main/reference/h_append_grade_groups.html index 2776a060c9..40ba81d7bd 100644 --- a/main/reference/h_append_grade_groups.html +++ b/main/reference/h_append_grade_groups.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html index 06b97cf510..cbaecea676 100644 --- a/main/reference/h_col_indices.html +++ b/main/reference/h_col_indices.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html index 36c0c9a1e1..5d58afe576 100644 --- a/main/reference/h_count_cumulative.html +++ b/main/reference/h_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html index 4be3ea8680..2300696d50 100644 --- a/main/reference/h_cox_regression.html +++ b/main/reference/h_cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html index 7ac8b43b49..54b0d7080d 100644 --- a/main/reference/h_data_plot.html +++ b/main/reference/h_data_plot.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html index 4cf323ad4e..96502e49d1 100644 --- a/main/reference/h_decompose_gg.html +++ b/main/reference/h_decompose_gg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html index 1033ff5a0c..11071684bb 100644 --- a/main/reference/h_format_row.html +++ b/main/reference/h_format_row.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -95,7 +65,7 @@ Argumentsformat (named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter. diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html index ba609f3d69..d4c8203cba 100644 --- a/main/reference/h_g_ipp.html +++ b/main/reference/h_g_ipp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ggkm.html b/main/reference/h_ggkm.html index 512f6a9180..0aac0f9127 100644 --- a/main/reference/h_ggkm.html +++ b/main/reference/h_ggkm.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_glm_count.html b/main/reference/h_glm_count.html index 036db54acf..74a4e7900b 100644 --- a/main/reference/h_glm_count.html +++ b/main/reference/h_glm_count.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html index 7dcead7d01..132d15c26a 100644 --- a/main/reference/h_grob_coxph.html +++ b/main/reference/h_grob_coxph.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html index 82ea2d549f..60bd8ff8d5 100644 --- a/main/reference/h_grob_median_surv.html +++ b/main/reference/h_grob_median_surv.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html index 3af28455fb..7879a81306 100644 --- a/main/reference/h_grob_tbl_at_risk.html +++ b/main/reference/h_grob_tbl_at_risk.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html index 48a9227ad3..dd46627d90 100644 --- a/main/reference/h_grob_y_annot.html +++ b/main/reference/h_grob_y_annot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html index 77eb4bd003..e85621d716 100644 --- a/main/reference/h_incidence_rate.html +++ b/main/reference/h_incidence_rate.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html index 9e3fdc86ed..67d651af4a 100644 --- a/main/reference/h_km_layout.html +++ b/main/reference/h_km_layout.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html index fe6cbfe730..8dd16febea 100644 --- a/main/reference/h_logistic_regression.html +++ b/main/reference/h_logistic_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html index 10495898fc..59f19b1f76 100644 --- a/main/reference/h_map_for_count_abnormal.html +++ b/main/reference/h_map_for_count_abnormal.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html index e1e17eedc3..b93837e390 100644 --- a/main/reference/h_odds_ratio.html +++ b/main/reference/h_odds_ratio.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html index 982b2989b6..318bbf2ecc 100644 --- a/main/reference/h_pkparam_sort.html +++ b/main/reference/h_pkparam_sort.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ppmeans.html b/main/reference/h_ppmeans.html index 0ce7208de8..57fe7b7773 100644 --- a/main/reference/h_ppmeans.html +++ b/main/reference/h_ppmeans.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html index 82c09a19d9..df5cd7a011 100644 --- a/main/reference/h_prop_diff.html +++ b/main/reference/h_prop_diff.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html index 879561bbee..7ebaf8c228 100644 --- a/main/reference/h_prop_diff_test.html +++ b/main/reference/h_prop_diff_test.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_proportions.html b/main/reference/h_proportions.html index e2454b4cec..cae1aa23c0 100644 --- a/main/reference/h_proportions.html +++ b/main/reference/h_proportions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_biomarkers_subgroups.html b/main/reference/h_response_biomarkers_subgroups.html index 6253742dc3..d40c44e05a 100644 --- a/main/reference/h_response_biomarkers_subgroups.html +++ b/main/reference/h_response_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_subgroups.html b/main/reference/h_response_subgroups.html index f8553643c3..3277639d7f 100644 --- a/main/reference/h_response_subgroups.html +++ b/main/reference/h_response_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_by_subgroups.html b/main/reference/h_split_by_subgroups.html index 30b5234a3f..53bbd7df94 100644 --- a/main/reference/h_split_by_subgroups.html +++ b/main/reference/h_split_by_subgroups.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html index 1c85bd0acc..f531b50248 100644 --- a/main/reference/h_split_param.html +++ b/main/reference/h_split_param.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_stack_by_baskets.html b/main/reference/h_stack_by_baskets.html index 856a6bfda2..6f466ed562 100644 --- a/main/reference/h_stack_by_baskets.html +++ b/main/reference/h_stack_by_baskets.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_step.html b/main/reference/h_step.html index 22264810df..aed7400437 100644 --- a/main/reference/h_step.html +++ b/main/reference/h_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html index e2ef402488..24d8bdff48 100644 --- a/main/reference/h_survival_biomarkers_subgroups.html +++ b/main/reference/h_survival_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html index f45ef9d1ed..27a3f5c549 100644 --- a/main/reference/h_survival_duration_subgroups.html +++ b/main/reference/h_survival_duration_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html index ca44867180..75abc5d17b 100644 --- a/main/reference/h_tab_one_biomarker.html +++ b/main/reference/h_tab_one_biomarker.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups(). +This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html index fb85cb68ff..d06f1ec802 100644 --- a/main/reference/h_tbl_coxph_pairwise.html +++ b/main/reference/h_tbl_coxph_pairwise.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html index 824c9b6ba7..6c36b258c8 100644 --- a/main/reference/h_tbl_median_surv.html +++ b/main/reference/h_tbl_median_surv.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html index bd3da774c9..75cee2d4f8 100644 --- a/main/reference/h_worsen_counter.html +++ b/main/reference/h_worsen_counter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html index 3b00b0578e..cdca04f44b 100644 --- a/main/reference/h_xticks.html +++ b/main/reference/h_xticks.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html index 4af234fef7..9793443aea 100644 --- a/main/reference/imputation_rule.html +++ b/main/reference/imputation_rule.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html index 54c857ff9c..c6a5c87c16 100644 --- a/main/reference/incidence_rate.html +++ b/main/reference/incidence_rate.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -239,7 +209,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -251,7 +221,7 @@ ArgumentsValue estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout. s_incidence_rate() returns the following statistics:person_years: Total person-years at risk. n_events: Total number of events observed. @@ -260,13 +230,13 @@ Valuen_unique: Total number of patients with at least one event observed. n_rate: Total number of events observed & estimated incidence rate. -a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). +a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval. a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate(). diff --git a/main/reference/index.html b/main/reference/index.html index 2a1665f99f..09b3124477 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -196,7 +166,7 @@ Analysis FunctionsAnalyze functions with their corresponding statistics functions and formatted analysis functions.Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table. Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves. -Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). +Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html index 3f1016c509..dd93cf81ce 100644 --- a/main/reference/labels_or_names.html +++ b/main/reference/labels_or_names.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/labels_use_control.html b/main/reference/labels_use_control.html index 5c301b4228..d4006f99f7 100644 --- a/main/reference/labels_use_control.html +++ b/main/reference/labels_use_control.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html index 7744090684..8accd2e89b 100644 --- a/main/reference/logistic_regression_cols.html +++ b/main/reference/logistic_regression_cols.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar(). +regression results. This function is a wrapper for rtables::split_cols_by_multivar(). diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html index 9807a24c22..45cb84b416 100644 --- a/main/reference/logistic_summary_by_flag.html +++ b/main/reference/logistic_summary_by_flag.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). +logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/make_names.html b/main/reference/make_names.html index d14c4af69d..ef3f426440 100644 --- a/main/reference/make_names.html +++ b/main/reference/make_names.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/month2day.html b/main/reference/month2day.html index 87004ac14c..bb59b07ac8 100644 --- a/main/reference/month2day.html +++ b/main/reference/month2day.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/muffled_car_anova.html b/main/reference/muffled_car_anova.html index 3d902766c3..e4c73d2ed5 100644 --- a/main/reference/muffled_car_anova.html +++ b/main/reference/muffled_car_anova.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/n_available.html b/main/reference/n_available.html index af70552f25..d8856b8358 100644 --- a/main/reference/n_available.html +++ b/main/reference/n_available.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html index 3bcb51179d..58a1801d4a 100644 --- a/main/reference/odds_ratio.html +++ b/main/reference/odds_ratio.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -228,17 +198,17 @@ ArgumentsValue estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout. s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot. -a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). +a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required. diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html index 51e9d86731..bd76dd200f 100644 --- a/main/reference/prop_diff.html +++ b/main/reference/prop_diff.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -237,16 +207,16 @@ ArgumentsValue estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout. s_proportion_diff() returns a named list of elements diff and diff_ci. -a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion. a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff(). diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html index 17bf534958..d4bfbab57f 100644 --- a/main/reference/prop_diff_test.html +++ b/main/reference/prop_diff_test.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,17 +187,17 @@ ArgumentsValue test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout. s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same. -a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_test_proportion_diff(): Statistics function which tests the difference between two proportions. a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff(). diff --git a/main/reference/prune_occurrences.html b/main/reference/prune_occurrences.html index 74e764167c..75f049f5b1 100644 --- a/main/reference/prune_occurrences.html +++ b/main/reference/prune_occurrences.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -137,7 +107,7 @@ Arguments Value -keep_rows() returns a pruning function that can be used with rtables::prune_table() +keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table. keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table. @@ -180,7 +150,7 @@ FunctionsNote Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface. +should be pruned, to conform with the rtables::prune_table() interface. diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html index ccf69253f9..8bcad27b16 100644 --- a/main/reference/range_noinf.html +++ b/main/reference/range_noinf.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html index 3369960273..c35e1c50c9 100644 --- a/main/reference/reapply_varlabels.html +++ b/main/reference/reapply_varlabels.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html index 53816fe9b9..4c72961f2a 100644 --- a/main/reference/response_biomarkers_subgroups.html +++ b/main/reference/response_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html index fb6d448883..a3f14ce1b6 100644 --- a/main/reference/response_subgroups.html +++ b/main/reference/response_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -171,7 +141,7 @@ Arguments Value An rtables table summarizing binary response by subgroup. -a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -182,8 +152,8 @@ DetailsFunctions tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups(). diff --git a/main/reference/rtable2gg.html b/main/reference/rtable2gg.html index 6068cb1bb8..cdf46a633f 100644 --- a/main/reference/rtable2gg.html +++ b/main/reference/rtable2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,7 +51,7 @@ -Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored. diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html index 0f6db1ef4c..67c8394ca2 100644 --- a/main/reference/rtables_access.html +++ b/main/reference/rtables_access.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html index 2198ad15a7..445c4fd861 100644 --- a/main/reference/s_cox_multivariate.html +++ b/main/reference/s_cox_multivariate.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html index fa683b677a..8c4b03b9ab 100644 --- a/main/reference/sas_na.html +++ b/main/reference/sas_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html index f5b86fe70a..3815c64608 100644 --- a/main/reference/score_occurrences.html +++ b/main/reference/score_occurrences.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Functionsrtables::cont_n_onecol() and rtables::cont_n_allcols(). +specified columns. Note that this is extending rtables::cont_n_onecol() and rtables::cont_n_allcols(). See also diff --git a/main/reference/split_cols_by_groups.html b/main/reference/split_cols_by_groups.html index 3c97f5cfee..f7bfcad622 100644 --- a/main/reference/split_cols_by_groups.html +++ b/main/reference/split_cols_by_groups.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -107,7 +77,7 @@ Arguments... -additional arguments to rtables::split_cols_by() in order. For instance, to +additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all). @@ -119,7 +89,7 @@ Value See also - rtables::split_cols_by() + rtables::split_cols_by() diff --git a/main/reference/split_text_grob.html b/main/reference/split_text_grob.html index 8671c0d8d3..062cbc39d2 100644 --- a/main/reference/split_text_grob.html +++ b/main/reference/split_text_grob.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html index fb97a58911..6f44c5774b 100644 --- a/main/reference/stack_grobs.html +++ b/main/reference/stack_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html index ffa8368b70..c31e318dd4 100644 --- a/main/reference/stat_mean_ci.html +++ b/main/reference/stat_mean_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html index ef90542a4e..6663e7ee57 100644 --- a/main/reference/stat_mean_pval.html +++ b/main/reference/stat_mean_pval.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_median_ci.html b/main/reference/stat_median_ci.html index ea5d28e072..f43024d65c 100644 --- a/main/reference/stat_median_ci.html +++ b/main/reference/stat_median_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_propdiff_ci.html b/main/reference/stat_propdiff_ci.html index b7119ac644..5db044c950 100644 --- a/main/reference/stat_propdiff_ci.html +++ b/main/reference/stat_propdiff_ci.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -141,7 +111,7 @@ Value See also - Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() + Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout. diff --git a/main/reference/strata_normal_quantile.html b/main/reference/strata_normal_quantile.html index afc45b01e1..82d6de6e3f 100644 --- a/main/reference/strata_normal_quantile.html +++ b/main/reference/strata_normal_quantile.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/study_arm.html b/main/reference/study_arm.html index 4a60dcedf5..67999fc7aa 100644 --- a/main/reference/study_arm.html +++ b/main/reference/study_arm.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/summarize_ancova.html b/main/reference/summarize_ancova.html index 1bf16f8b79..1eba34c24c 100644 --- a/main/reference/summarize_ancova.html +++ b/main/reference/summarize_ancova.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -254,7 +224,7 @@ ArgumentsValue summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout. s_ancova() returns a named list of 5 statistics:n: Count of complete sample size for the group. lsmean: Estimated marginal means in the group. @@ -264,13 +234,13 @@ Value pval: p-value (not adjusted for multiple comparisons). -a_ancova() returns the corresponding list with formatted rtables::CellValue(). +a_ancova() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_ancova(): Statistics function that produces a named list of results of the investigated linear model. a_ancova(): Formatted analysis function which is used as afun in summarize_ancova(). diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html index 70d34fcade..83fc422b89 100644 --- a/main/reference/summarize_change.html +++ b/main/reference/summarize_change.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -192,16 +162,16 @@ ArgumentsValue summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout. s_change_from_baseline() returns the same values returned by s_summary.numeric(). -a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits. a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change(). diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html index 5f7d10a3ef..2caae0ef34 100644 --- a/main/reference/summarize_colvars.html +++ b/main/reference/summarize_colvars.html @@ -2,13 +2,13 @@ Summarize variables in columns — summarize_colvars • tern @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -91,8 +61,8 @@ The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars(). -The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars(). +The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function. @@ -147,13 +117,13 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. See also - rtables::split_cols_by_multivar() and analyze_colvars_functions. + rtables::split_cols_by_multivar() and analyze_colvars_functions. diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html index 751617921c..34ebaf2237 100644 --- a/main/reference/summarize_functions.html +++ b/main/reference/summarize_functions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -78,7 +48,7 @@ - These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions + These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout: @@ -87,7 +57,7 @@ Details add_rowcounts() -estimate_multinomial_response() (with rtables::analyze()) +estimate_multinomial_response() (with rtables::analyze()) h_tab_one_biomarker() (probably to deprecate) logistic_summary_by_flag() summarize_num_patients() @@ -95,15 +65,15 @@ Detailssummarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() -Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table. See also -analyze_functions for functions which are wrappers for rtables::analyze(). -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +analyze_functions for functions which are wrappers for rtables::analyze(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). diff --git a/main/reference/summarize_glm_count.html b/main/reference/summarize_glm_count.html index 112cce09c8..6f424e2306 100644 --- a/main/reference/summarize_glm_count.html +++ b/main/reference/summarize_glm_count.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -252,7 +222,7 @@ ArgumentsValue summarize_glm_count() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_glm_count() to the table layout. s_glm_count() returns a named list of 5 statistics:n: Count of complete sample size for the group. rate: Estimated event rate per follow-up time. @@ -280,7 +250,7 @@ DetailsFunctions summarize_glm_count(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_glm_count(): Statistics function that produces a named list of results of the investigated Poisson model. diff --git a/main/reference/summarize_logistic.html b/main/reference/summarize_logistic.html index 328200b769..92bbe7b17b 100644 --- a/main/reference/summarize_logistic.html +++ b/main/reference/summarize_logistic.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -139,7 +109,7 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add a logistic regression variable summary to the table layout. diff --git a/main/reference/summarize_num_patients.html b/main/reference/summarize_num_patients.html index 275721ff71..1591a53f8f 100644 --- a/main/reference/summarize_num_patients.html +++ b/main/reference/summarize_num_patients.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -233,7 +203,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -255,10 +225,10 @@ ArgumentsValue analyze_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout. summarize_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout. s_num_patients() returns a named list of 3 statistics:unique: Vector of counts and percentages. nonunique: Vector of counts. @@ -269,8 +239,8 @@ Value Details In general, functions that starts with analyze* are expected to -work like rtables::analyze(), while functions that starts with summarize* -are based upon rtables::summarize_row_groups(). The latter provides a +work like rtables::analyze(), while functions that starts with summarize* +are based upon rtables::summarize_row_groups(). The latter provides a value for each dividing split in the row and column space, but, being it bound to the fundamental splits, it is repeated by design in every page when pagination is involved. @@ -279,9 +249,9 @@ DetailsFunctions analyze_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_num_patients(): Statistics function which counts the number of unique patients, the corresponding percentage taken with respect to the total number of patients, and the number of non-unique patients. diff --git a/main/reference/summarize_patients_exposure_in_cols.html b/main/reference/summarize_patients_exposure_in_cols.html index ffe89cefda..3cab97375f 100644 --- a/main/reference/summarize_patients_exposure_in_cols.html +++ b/main/reference/summarize_patients_exposure_in_cols.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -234,7 +204,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -252,32 +222,32 @@ ArgumentsValue analyze_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted data rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout. summarize_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout. s_count_patients_sum_exposure() returns a named list with the statistics:n_patients: Number of unique patients in df. sum_exposure: Sum of ex_var across all patients in df. -a_count_patients_sum_exposure() returns formatted rtables::CellValue(). +a_count_patients_sum_exposure() returns formatted rtables::CellValue(). Functions analyze_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::analyze_colvars(). +rtables::split_cols_by_multivar() and rtables::analyze_colvars(). summarize_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::summarize_row_groups(). +rtables::split_cols_by_multivar() and rtables::summarize_row_groups(). s_count_patients_sum_exposure(): Statistics function which counts numbers of patients and the sum of exposure across all patients. a_count_patients_sum_exposure(): Analysis function which is used as afun in -rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in -rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). +rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in +rtables::summarize_row_groups() within summarize_patients_exposure_in_cols(). Note diff --git a/main/reference/survival_biomarkers_subgroups.html b/main/reference/survival_biomarkers_subgroups.html index 91a000d62d..3e83a8ea13 100644 --- a/main/reference/survival_biomarkers_subgroups.html +++ b/main/reference/survival_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/survival_coxph_pairwise.html b/main/reference/survival_coxph_pairwise.html index 5112ec55c3..30bac141b4 100644 --- a/main/reference/survival_coxph_pairwise.html +++ b/main/reference/survival_coxph_pairwise.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -235,7 +205,7 @@ ArgumentsValue coxph_pairwise() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_coxph_pairwise() to the table layout. s_coxph_pairwise() returns the statistics:pvalue: p-value to test the null hypothesis that hazard ratio = 1. hr: Hazard ratio. @@ -243,13 +213,13 @@ Valuen_tot: Total number of observations. n_tot_events: Total number of events. -a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue(). +a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue(). Functions coxph_pairwise(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_coxph_pairwise(): Statistics function which analyzes HR, CIs of HR, and p-value of a Cox-PH model. a_coxph_pairwise(): Formatted analysis function which is used as afun in coxph_pairwise(). diff --git a/main/reference/survival_duration_subgroups.html b/main/reference/survival_duration_subgroups.html index cd75957453..6b1da60bf3 100644 --- a/main/reference/survival_duration_subgroups.html +++ b/main/reference/survival_duration_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -179,7 +149,7 @@ Arguments Value An rtables table summarizing survival by subgroup. -a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -190,8 +160,8 @@ DetailsFunctions tabulate_survival_subgroups(): Table-creating function which creates a table -summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_survival_subgroups(): Formatted analysis function which is used as afun in tabulate_survival_subgroups(). diff --git a/main/reference/survival_time.html b/main/reference/survival_time.html index 0c035af7aa..218c910f45 100644 --- a/main/reference/survival_time.html +++ b/main/reference/survival_time.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,7 +187,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -229,7 +199,7 @@ ArgumentsValue surv_time() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_time() to the table layout. s_surv_time() returns the statistics:median: Median survival time. median_ci: Confidence interval for median time. @@ -238,13 +208,13 @@ Valuerange_event: Survival time range for observations with events. range: Survival time range for all observations. -a_surv_time() returns the corresponding list with formatted rtables::CellValue(). +a_surv_time() returns the corresponding list with formatted rtables::CellValue(). Functions surv_time(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_surv_time(): Statistics function which analyzes survival times. a_surv_time(): Formatted analysis function which is used as afun in surv_time(). diff --git a/main/reference/survival_timepoint.html b/main/reference/survival_timepoint.html index 8aaa066601..ec25ceb866 100644 --- a/main/reference/survival_timepoint.html +++ b/main/reference/survival_timepoint.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -262,7 +232,7 @@ ArgumentsValue surv_timepoint() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_timepoint() and/or s_surv_timepoint_diff() to the table layout depending on the value of method. s_surv_timepoint() returns the statistics:pt_at_risk: Patients remaining at risk. @@ -270,18 +240,18 @@ Valuerate_se: Standard error of event free rate. rate_ci: Confidence interval for event free rate. -a_surv_timepoint() returns the corresponding list with formatted rtables::CellValue(). +a_surv_timepoint() returns the corresponding list with formatted rtables::CellValue(). s_surv_timepoint_diff() returns the statistics:rate_diff: Event-free rate difference between two groups. rate_diff_ci: Confidence interval for the difference. ztest_pval: p-value to test the difference is 0. -a_surv_timepoint_diff() returns the corresponding list with formatted rtables::CellValue(). +a_surv_timepoint_diff() returns the corresponding list with formatted rtables::CellValue(). Functions surv_timepoint(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_surv_timepoint(): Statistics function which analyzes survival rate. a_surv_timepoint(): Formatted analysis function which is used as afun in surv_timepoint() when method = "surv". diff --git a/main/reference/tern-package.html b/main/reference/tern-package.html index 786dc13bae..f874f57a46 100644 --- a/main/reference/tern-package.html +++ b/main/reference/tern-package.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy.glm.html b/main/reference/tidy.glm.html index 8ba67828d5..f38573bb34 100644 --- a/main/reference/tidy.glm.html +++ b/main/reference/tidy.glm.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy.step.html b/main/reference/tidy.step.html index 3102bfd85c..62d83532eb 100644 --- a/main/reference/tidy.step.html +++ b/main/reference/tidy.step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/tidy_coxreg.html b/main/reference/tidy_coxreg.html index b5f23d9fb1..8c582bd258 100644 --- a/main/reference/tidy_coxreg.html +++ b/main/reference/tidy_coxreg.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/to_n.html b/main/reference/to_n.html index 7f5e1974e8..251d0c8988 100644 --- a/main/reference/to_n.html +++ b/main/reference/to_n.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/to_string_matrix.html b/main/reference/to_string_matrix.html index f89d568b37..626812db9c 100644 --- a/main/reference/to_string_matrix.html +++ b/main/reference/to_string_matrix.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/try_car_anova.html b/main/reference/try_car_anova.html index c284bc2d74..4ed62e0aa7 100644 --- a/main/reference/try_car_anova.html +++ b/main/reference/try_car_anova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/ungroup_stats.html b/main/reference/ungroup_stats.html index 19b4f9085b..b4e4fcce37 100644 --- a/main/reference/ungroup_stats.html +++ b/main/reference/ungroup_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/univariate.html b/main/reference/univariate.html index 0f9eb3bf68..aebc9f7618 100644 --- a/main/reference/univariate.html +++ b/main/reference/univariate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/unlist_and_blank_na.html b/main/reference/unlist_and_blank_na.html index d567cadc7b..7b4e397835 100644 --- a/main/reference/unlist_and_blank_na.html +++ b/main/reference/unlist_and_blank_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/update_weights_strat_wilson.html b/main/reference/update_weights_strat_wilson.html index 9d916b424f..5b1ae9ab57 100644 --- a/main/reference/update_weights_strat_wilson.html +++ b/main/reference/update_weights_strat_wilson.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/utils_split_funs.html b/main/reference/utils_split_funs.html index 3346cc2b60..064b48c0b0 100644 --- a/main/reference/utils_split_funs.html +++ b/main/reference/utils_split_funs.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -88,10 +58,10 @@ Collection of useful functions that are expanding on the core list of functions -provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() +provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() for more information on how to make a custom split function. All these functions -work with rtables::split_rows_by() argument split_fun to modify the way the split -happens. For other split functions, consider consulting rtables::split_funcs. +work with rtables::split_rows_by() argument split_fun to modify the way the split +happens. For other split functions, consider consulting rtables::split_funcs. @@ -132,7 +102,7 @@ Functions See also - rtables::make_split_fun() + rtables::make_split_fun() diff --git a/main/search.json b/main/search.json index 370f686740..283494a4a4 100644 --- a/main/search.json +++ b/main/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution Guidelines","title":"Contribution Guidelines","text":"🙏 Thank taking time contribute! input deeply valued, whether issue, pull request, even feedback, regardless size, content scope.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of contents","title":"Contribution Guidelines","text":"👶 Getting started 📔 Code Conduct 🗃 License 📜 Issues 🚩 Pull requests 💻 Coding guidelines 🏆 Recognition model ❓ Questions","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Contribution Guidelines","text":"Please refer project documentation brief introduction. Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: _. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: __. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/tern/main/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"variable-class-conversion","dir":"Articles","previous_headings":"","what":"Variable Class Conversion","title":"Missing Values in Tern","text":"rtables requires split variables factors. try split variable isn’t, warning message appear. purposefully convert SEX variable character demonstrate happens try splitting rows variable. fix , df_explict_na convert factor resulting table generated.","code":"adsl <- tern_ex_adsl adsl$SEX <- as.character(adsl$SEX) vars <- c(\"AGE\", \"SEX\", \"RACE\", \"BMRKR1\") var_labels <- c( \"Age (yr)\", \"Sex\", \"Race\", \"Continous Level Biomarker 1\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures #> Warning in as_factor_keep_attributes(x, verbose = verbose): automatically #> converting character variable x to factor, better manually convert to factor to #> avoid failures result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%) #> Race #> n 69 73 58 200 #> ASIAN 38 (55.1%) 43 (58.9%) 29 (50%) 110 (55%) #> BLACK OR AFRICAN AMERICAN 15 (21.7%) 13 (17.8%) 12 (20.7%) 40 (20%) #> WHITE 11 (15.9%) 12 (16.4%) 11 (19%) 34 (17%) #> AMERICAN INDIAN OR ALASKA NATIVE 4 (5.8%) 3 (4.1%) 6 (10.3%) 13 (6.5%) #> MULTIPLE 1 (1.4%) 1 (1.4%) 0 2 (1%) #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (1.4%) 0 1 (0.5%) #> OTHER 0 0 0 0 #> UNKNOWN 0 0 0 0 #> Continous Level Biomarker 1 #> n 69 73 58 200 #> Mean (SD) 6.3 (3.6) 6.7 (3.5) 6.2 (3.3) 6.4 (3.5) #> Median 5.4 6.3 5.4 5.6 #> Min - Max 0.4 - 17.8 1.0 - 18.5 2.4 - 19.1 0.4 - 19.1"},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"including-missing-values-in-rtables","dir":"Articles","previous_headings":"","what":"Including Missing Values in rtables","title":"Missing Values in Tern","text":"purposefully convert M values NA SEX variable. running df_explicit_na NA values encoded included table. well, missing values included n count included denominator value calculating percent values. want Na values displayed table included n count denominator calculating percent values, use na_level argument.","code":"adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl) vars <- c(\"AGE\", \"SEX\") var_labels <- c( \"Age (yr)\", \"Sex\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 38 40 32 110 #> F 38 (100%) 40 (100%) 32 (100%) 110 (100%) #> M 0 0 0 0 adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\") result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ———————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 69 73 58 200 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) 35.3 (7.1) #> Median 32.8 35.4 36.2 34.8 #> Min - Max 22.4 - 48.0 23.3 - 57.5 23.0 - 58.3 22.4 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 0 0 0 0 #> Missing Values 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/missing_values.html","id":"missing-values-in-numeric-variables","dir":"Articles","previous_headings":"","what":"Missing Values in Numeric Variables","title":"Missing Values in Tern","text":"Numeric variables missing values altered. means NA value numeric variable included summary statistics, included denominator value calculating percent values. make value less 30 missing AGE variable valued greater 30 included table .","code":"adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl) vars <- c(\"AGE\", \"SEX\") var_labels <- c( \"Age (yr)\", \"Sex\" ) result <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) result #> A: Drug X B: Placebo C: Combination All Patients #> (N=69) (N=73) (N=58) (N=200) #> ——————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 46 56 44 146 #> Mean (SD) 37.8 (5.2) 38.3 (6.3) 39.1 (5.9) 38.3 (5.8) #> Median 37.2 37.3 37.5 37.5 #> Min - Max 30.3 - 48.0 30.0 - 57.5 30.5 - 58.3 30.0 - 58.3 #> Sex #> n 69 73 58 200 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) 110 (55%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) 90 (45%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tern-tabulation","dir":"Articles","previous_headings":"","what":"tern Tabulation","title":"Tabulation","text":"tern R package provides functions create common analyses clinical trials R. core functionality tabulation built general purpose rtables package. New users first begin reading “Introduction tern” “Introduction rtables” vignettes. packages used vignette : datasets used vignette :","code":"library(rtables) library(tern) library(dplyr) adsl <- ex_adsl adae <- ex_adae adrs <- ex_adrs"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tern-analyze-functions","dir":"Articles","previous_headings":"","what":"tern Analyze Functions","title":"Tabulation","text":"Analyze functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analyze functions wrappers around rtables::analyze function, offer various methods useful perspective clinical trials statistical projects. Examples tern analyze functions count_occurrences, summarize_ancova analyze_vars. one prefix identify tern analyze functions recommended use tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"internals-of-tern-analyze-functions","dir":"Articles","previous_headings":"tern Analyze Functions","what":"Internals of tern Analyze Functions","title":"Tabulation","text":"Please skip subsection interested internals tern analyze functions. Internally tern analyze functions like summarize_ancova mainly built 4 elements chain: descriptions function type: analysis helper functions h_*. functions useful help define analysis. statistics function s_*. Statistics functions computation numbers tabulated later. order separate computation formatting, take care rcell type formatting . formatted analysis functions a_*. arguments corresponding statistics functions, can customized calling rtables::make_afun() . used afun rtables::analyze(). analyze functions rtables::analyze(..., afun = make_afun(tern::a_*)). Analyze functions used combination rtables layout functions, pipeline creates table. last element chain. use native rtables::analyze function tern formatted analysis functions afun parameter. rtables::make_afun function helpful somebody wants attach format formatted analysis function.","code":"h_ancova() -> tern:::s_ancova() -> tern:::a_ancova() -> summarize_ancova() l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze(vars = \"AVAL\", afun = a_summary) build_table(l, df = adrs) afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = \"xx.\"), .labels = c(median = \"My median\"), .indent_mods = c(median = 1L) ) l2 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze(vars = \"AVAL\", afun = afun) build_table(l2, df = adrs)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"tabulation-examples","dir":"Articles","previous_headings":"","what":"Tabulation Examples","title":"Tabulation","text":"going create 3 different tables using tern analyze functions rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"demographic-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Demographic Table","title":"Tabulation","text":"Demographic tables provide summary characteristics patients enrolled clinical trial. Typically table columns represent treatment arms variables summarized table demographic properties age, sex, race, etc. example function tern analyze_vars() remaining layout functions rtables. change display order categorical variables table use factor variables explicitly set order levels. case display order columns rows. Note forcats package many useful functions help types data processing steps (used ). tern package includes many functions similar analyze_vars(). functions called layout creating functions used combination rtables layout functions just like examples . Layout creating functions wrapping calls rtables analyze(), analyze_colvars() summarize_row_groups() provide options easy formatting analysis modifications. customize display demographics table, can via arguments analyze_vars(). layout creating functions tern include standard arguments .stats, .formats, .labels .indent_mods control statistics displayed numbers formatted. Refer package help help(\"analyze_vars\") ?analyze_vars see full set options. example change default summary numeric variables include number records, mean standard deviation (single statistic, .e. within single cell). categorical variables modify summary include number records counts categories. also modify display format mean standard deviation print two decimal places instead just one. One feature layout can used different datasets create different summaries. example, can easily create summary demographics Brazil China subgroups, respectively:","code":"# Select variables to include in table. vars <- c(\"AGE\", \"SEX\") var_labels <- c(\"Age (yr)\", \"Sex\") basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7) 34.9 (7.4) #> Median 33.0 35.0 35.0 34.0 #> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0 20.0 - 69.0 #> Sex #> n 134 134 132 400 #> F 79 (59%) 77 (57.5%) 66 (50%) 222 (55.5%) #> M 51 (38.1%) 55 (41%) 60 (45.5%) 166 (41.5%) #> U 3 (2.2%) 2 (1.5%) 4 (3%) 9 (2.2%) #> UNDIFFERENTIATED 1 (0.7%) 0 2 (1.5%) 3 (0.8%) # Reorder the levels in the ARM variable. adsl$ARM <- factor(adsl$ARM, levels = c(\"B: Placebo\", \"A: Drug X\", \"C: Combination\")) # Reorder the levels in the SEX variable. adsl$SEX <- factor(adsl$SEX, levels = c(\"M\", \"F\", \"U\", \"UNDIFFERENTIATED\")) basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) %>% build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 35.4 (7.9) 33.8 (6.6) 35.4 (7.7) 34.9 (7.4) #> Median 35.0 33.0 35.0 34.0 #> Min - Max 21.0 - 62.0 21.0 - 50.0 20.0 - 69.0 20.0 - 69.0 #> Sex #> n 134 134 132 400 #> M 55 (41%) 51 (38.1%) 60 (45.5%) 166 (41.5%) #> F 77 (57.5%) 79 (59%) 66 (50%) 222 (55.5%) #> U 2 (1.5%) 3 (2.2%) 4 (3%) 9 (2.2%) #> UNDIFFERENTIATED 0 1 (0.7%) 2 (1.5%) 3 (0.8%) # Select statistics and modify default formats. basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels, .stats = c(\"n\", \"mean_sd\", \"count\"), .formats = c(mean_sd = \"xx.xx (xx.xx)\") ) %>% build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ———————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 134 134 132 400 #> Mean (SD) 35.43 (7.90) 33.77 (6.55) 35.43 (7.72) 34.88 (7.44) #> Sex #> n 134 134 132 400 #> M 55 51 60 166 #> F 77 79 66 222 #> U 2 3 4 9 #> UNDIFFERENTIATED 0 1 2 3 lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% add_overall_col(\"All Patients\") %>% add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"BRA\")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 7 13 10 30 #> Mean (SD) 32.0 (6.1) 36.7 (6.4) 38.3 (10.6) 36.1 (8.1) #> Median 32.0 37.0 35.0 35.5 #> Min - Max 25.0 - 42.0 24.0 - 47.0 25.0 - 64.0 24.0 - 64.0 #> Sex #> n 7 13 10 30 #> M 4 (57.1%) 8 (61.5%) 5 (50%) 17 (56.7%) #> F 3 (42.9%) 5 (38.5%) 5 (50%) 13 (43.3%) #> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == \"CHN\")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— #> Age (yr) #> n 81 74 64 219 #> Mean (SD) 35.7 (7.3) 33.0 (6.4) 35.2 (6.4) 34.6 (6.8) #> Median 36.0 32.0 35.0 34.0 #> Min - Max 21.0 - 58.0 23.0 - 48.0 21.0 - 49.0 21.0 - 58.0 #> Sex #> n 81 74 64 219 #> M 35 (43.2%) 27 (36.5%) 30 (46.9%) 92 (42%) #> F 45 (55.6%) 44 (59.5%) 29 (45.3%) 118 (53.9%) #> U 1 (1.2%) 2 (2.7%) 3 (4.7%) 6 (2.7%) #> UNDIFFERENTIATED 0 1 (1.4%) 2 (3.1%) 3 (1.4%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"adverse-event-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Adverse Event Table","title":"Tabulation","text":"standard table adverse events summary system organ class preferred term. frequency counts preferred term, multiple occurrences AE individual count . create table need use combination several layout creating functions tabulation pipeline. start creating high-level summary. layout creating function tern can summarize_num_patients(): Note table, denominator used percentages shown header table (N = xx) defined based subject-level dataset adsl. done using alt_df_counts argument build_table(), provides alternative data set deriving counts header. often required work data sets include multiple records per patient df, adae .","code":"basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"statistics-functions","dir":"Articles","previous_headings":"Tabulation Examples > Adverse Event Table","what":"Statistics Functions","title":"Tabulation","text":"building rest AE table helpful introduce tern package design conventions. layout creating function tern wrapper Statistics function. Statistics functions ones actual computation numbers table. functions always return named lists whose elements statistics available include layout via .stats argument layout creating function level. Statistics functions follow naming convention always begin s_* ease use documented page layout creating function counterpart. helpful review Statistic function understand logic used calculate numbers table see options may available modify analysis. example, Statistics function calculating numbers summarize_num_patients() s_num_patients(). results Statistics function list elements unique, nonunique unique_count: results can see unique nonunique statistics displayed “Patients” column initial AE table output . Also can see raw numbers formatted way. formatting functionality handled layout creating function level .formats argument. Now know types statistics can derived s_num_patients(), can try modifying default layout returned summarize_num_patients(). Instead reporting unique nonqunie statistics, specify analysis include unique_count statistic. result show counts unique patients. Note make update .stats .labels argument summarize_num_patients(). Let’s now continue building layout adverse event table. top-level summary, can repeat summary system organ class level. split analysis data split_rows_by() calling summarize_num_patients(). table looks almost ready. final step, need layout creating function can produce count table event frequencies. layout creating function count_occurrences(). Let’s first try using function simpler layout without row splits: Putting everything together, final AE table looks like :","code":"s_num_patients(x = adae$USUBJID, labelstr = \"\", .N_col = nrow(adae)) #> $unique #> [1] 365.000000 0.188728 #> attr(,\"label\") #> [1] \"\" #> #> $nonunique #> [1] 1934 #> attr(,\"label\") #> [1] \"\" #> #> $unique_count #> [1] 365 #> attr(,\"label\") #> [1] \"(n)\" basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\", .labels = c(unique_count = \"Total number of patients with at least one AE\") ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> —————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 123 120 365 basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% split_rows_by( \"AEBODSYS\", child_labels = \"visible\", nested = FALSE, indent_mod = -1L, split_fun = drop_split_levels ) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934 #> cl A.1 #> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%) #> Overall total number of events 132 130 160 422 #> cl B.1 #> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> Overall total number of events 56 60 62 178 #> cl B.2 #> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%) #> Overall total number of events 129 138 143 410 #> cl C.1 #> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> Overall total number of events 55 63 64 182 #> cl C.2 #> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> Overall total number of events 48 53 65 166 #> cl D.1 #> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%) #> Overall total number of events 127 106 135 368 #> cl D.2 #> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) #> Overall total number of events 62 72 74 208 basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% count_occurrences(vars = \"AEDECOD\") %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————— #> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%) #> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%) #> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%) #> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%) #> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%) #> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%) #> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) basic_table() %>% split_cols_by(var = \"ACTARM\") %>% add_colcounts() %>% add_overall_col(label = \"All Patients\") %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% split_rows_by( \"AEBODSYS\", child_labels = \"visible\", nested = FALSE, indent_mod = -1L, split_fun = drop_split_levels ) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( unique = \"Total number of patients with at least one AE\", nonunique = \"Overall total number of events\" ) ) %>% count_occurrences(vars = \"AEDECOD\") %>% build_table( df = adae, alt_counts_df = adsl ) #> A: Drug X B: Placebo C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one AE 122 (91.0%) 123 (91.8%) 120 (90.9%) 365 (91.2%) #> Overall total number of events 609 622 703 1934 #> cl A.1 #> Total number of patients with at least one AE 78 (58.2%) 75 (56.0%) 89 (67.4%) 242 (60.5%) #> Overall total number of events 132 130 160 422 #> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%) 158 (39.5%) #> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%) 146 (36.5%) #> cl B.1 #> Total number of patients with at least one AE 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> Overall total number of events 56 60 62 178 #> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%) 139 (34.8%) #> cl B.2 #> Total number of patients with at least one AE 79 (59.0%) 74 (55.2%) 85 (64.4%) 238 (59.5%) #> Overall total number of events 129 138 143 410 #> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%) 145 (36.2%) #> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%) 153 (38.2%) #> cl C.1 #> Total number of patients with at least one AE 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> Overall total number of events 55 63 64 182 #> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%) 132 (33.0%) #> cl C.2 #> Total number of patients with at least one AE 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> Overall total number of events 48 53 65 166 #> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%) 138 (34.5%) #> cl D.1 #> Total number of patients with at least one AE 79 (59.0%) 67 (50.0%) 80 (60.6%) 226 (56.5%) #> Overall total number of events 127 106 135 368 #> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%) 143 (35.8%) #> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%) 140 (35.0%) #> cl D.2 #> Total number of patients with at least one AE 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) #> Overall total number of events 62 72 74 208 #> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"response-table","dir":"Articles","previous_headings":"Tabulation Examples","what":"Response Table","title":"Tabulation","text":"typical response table binary clinical trial endpoint may composed several different analyses: Proportion responders treatment group Difference proportion responders comparison groups vs. control group Chi-Square test difference response rates comparison groups vs. control group can build table layout like following approach used AE table: table section produced using different layout creating function tern. First start data preparation steps set analysis dataset. select endpoint analyze PARAMCD define logical variable is_rsp indicates whether patient classified responder . create summary proportion responders treatment group, use estimate_proportion() layout creating function: specify arm table used reference, use argument ref_group split_cols_by(). change reference arm “B: Placebo” arm displayed first column: customize analysis, can use method conf_level arguments modify type confidence interval calculated: next table section needed summarize difference response rates reference arm comparison arm. Use estimate_proportion_diff() layout creating function : final section needed complete table includes statistical test difference response rates. Use test_proportion_diff() layout creating function : customize output, use method argument select Chi-Squared test Schouten correction. Now can put table sections together one layout pipeline. Note one small change needed. Since primary analysis variable table sections (is_rsp), need give sub-table unique name. done adding table_names argument providing unique names :","code":"# Preprocessing to select an analysis endpoint. anl <- adrs %>% dplyr::filter(PARAMCD == \"BESRSPI\") %>% dplyr::mutate(is_rsp = AVALC %in% c(\"CR\", \"PR\")) basic_table() %>% split_cols_by(var = \"ARM\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", table_names = \"est_prop\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", method = \"clopper-pearson\", conf_level = 0.9 ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion_diff( vars = \"is_rsp\", show_labels = \"visible\", var_labels = \"Unstratified Analysis\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— #> Unstratified Analysis #> Difference in Response rate (%) 17.9 23.7 #> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8) basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% test_proportion_diff(vars = \"is_rsp\") %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— #> p-value (Chi-Squared Test) 0.0006 <0.0001 basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% test_proportion_diff( vars = \"is_rsp\", method = \"schouten\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001 basic_table() %>% split_cols_by(var = \"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% estimate_proportion( vars = \"is_rsp\", method = \"clopper-pearson\", conf_level = 0.9, table_names = \"est_prop\" ) %>% estimate_proportion_diff( vars = \"is_rsp\", show_labels = \"visible\", var_labels = \"Unstratified Analysis\", table_names = \"est_prop_diff\" ) %>% test_proportion_diff( vars = \"is_rsp\", method = \"schouten\", table_names = \"test_prop_diff\" ) %>% build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> Responders 114 (85.1%) 90 (67.2%) 120 (90.9%) #> 90% CI (Clopper-Pearson) (79.1, 89.9) (59.9, 73.9) (85.7, 94.7) #> Unstratified Analysis #> Difference in Response rate (%) 17.9 23.7 #> 95% CI (Wald, with correction) (7.2, 28.6) (13.7, 33.8) #> p-value (Chi-Squared Test with Schouten Correction) 0.0008 <0.0001"},{"path":"https://insightsengineering.github.io/tern/main/articles/tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Tabulation","text":"Tabulation tern builds top layout tabulation framework rtables. Complex tables built step step pipeline combining layout creating functions perform specific type analysis. tern analyze functions introduced vignette : analyze_vars() summarize_num_patients() count_occurrences() estimate_proportion() estimate_proportion_diff() test_proportion_diff() Layout creating functions build formatted layout controlling features labels, numerical display formats indentation. functions wrappers Statistics functions calculate raw summaries analysis. can easily spot Statistics functions documentation always begin prefix s_. can helpful inspect run Statistics functions understand ways analysis can customized.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"introduction-to-tern","dir":"Articles","previous_headings":"","what":"Introduction to tern","title":"Introduction to tern","text":"vignette shows general purpose syntax tern R package. tern R package contains analytical functions creating tables graphs useful clinical trials statistical analysis. main focus clinical trial reporting tables graphs related clinical trials also valuable. core functionality tabulation built top general purpose rtables package. strongly recommended start reading “Introduction rtables” vignette get familiar concept rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"common-clinical-trials-analyses","dir":"Articles","previous_headings":"","what":"Common Clinical Trials Analyses","title":"Introduction to tern","text":"package provides large range functionality create tables graphs used clinical trial statistical analysis. rtables tabulation extended clinical trials specific functions: demographics unique patients exposure across patients change baseline parameters statistical model fits: MMRM, logistic regression, Cox regression, … … rtables tabulation helper functions: pre-processing conversions transformations … data visualizations connected clinical trials: Kaplan-Meier plots forest plots line plots … data visualizations helper functions: arrange/stack multiple graphs embellishing graphs/tables metadata details, adding titles, footnotes, page number, etc. … reference tern functions available tern website functions reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"analytical-functions-for-rtables","dir":"Articles","previous_headings":"","what":"Analytical Functions for rtables","title":"Introduction to tern","text":"Analytical functions used combination rtables layout functions, pipeline creates rtables table. apply statistical logic layout rtables table. table layout materialized rtables::build_table function data. tern analytical functions wrappers around rtables::analyze function; offer various methods useful perspective clinical trials statistical projects. Examples tern analytical functions count_occurrences, summarize_ancova analyze_vars. one prefix identify tern analytical functions recommended use reference subsection tern website. rtables code first describe two tables assign descriptions variables lyt lyt2. built tables using actual data rtables::build_table. description table called table layout. analyze instruction adds layout ARM variable analyzed mean analysis function result rounded 1 decimal place. Hence, layout “pre-data”; , ’s description build table get data. Defining table layout pure rtables code. tern function analyze_vars replaces rtables::analyze function . see tern offers advanced analysis extending rtables function calls one additional function call. examples tabulation analyze functions presented Tabulation vignette.","code":"library(tern) library(dplyr) # Create table layout pure rtables lyt <- rtables::basic_table() %>% rtables::split_cols_by(var = \"ARM\") %>% rtables::split_rows_by(var = \"AVISIT\") %>% rtables::analyze(vars = \"AVAL\", mean, format = \"xx.x\") # Create table layout with tern analyze_vars analyze function lyt2 <- rtables::basic_table() %>% rtables::split_cols_by(var = \"ARM\") %>% rtables::split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\", .formats = c(\"mean_sd\" = \"(xx.xx, xx.xx)\")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING #> mean 3.0 3.0 3.0 #> BASELINE #> mean 2.5 2.8 2.5 #> END OF INDUCTION #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING #> n 154 178 144 #> Mean (SD) (3.00, 0.00) (3.00, 0.00) (3.00, 0.00) #> Median 3.0 3.0 3.0 #> Min - Max 3.0 - 3.0 3.0 - 3.0 3.0 - 3.0 #> BASELINE #> n 136 146 124 #> Mean (SD) (2.46, 0.88) (2.77, 1.00) (2.46, 1.08) #> Median 3.0 3.0 3.0 #> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0 #> END OF INDUCTION #> n 218 205 217 #> Mean (SD) (1.75, 0.90) (2.14, 1.28) (1.65, 1.06) #> Median 2.0 2.0 1.0 #> Min - Max 1.0 - 4.0 1.0 - 5.0 1.0 - 5.0 #> FOLLOW UP #> n 164 153 167 #> Mean (SD) (2.23, 1.26) (2.89, 1.29) (1.97, 1.01) #> Median 2.0 4.0 2.0 #> Min - Max 1.0 - 4.0 1.0 - 4.0 1.0 - 4.0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"clinical-trials-visualizations","dir":"Articles","previous_headings":"","what":"Clinical Trials Visualizations","title":"Introduction to tern","text":"Clinical trial related plots complement rich palette tern tabulation analysis functions. Thus tern package delivers full-featured tool clinical trial reporting. tern plot functions return ggplot2 gTree objects, latter returned table attached plot. optional nestcolor package can loaded apply standardized NEST color palette tern plots. Line plot without table generated g_lineplot function. Line plot table generated g_lineplot function. first plot ggplot2 object second plot gTree object, latter contains table. second plot properly resized get clear readable table content. tern functions used plot generation mostly g_ prefixed. tern plot functions listed tern website functions reference.","code":"adsl <- formatters::ex_adsl adlb <- formatters::ex_adlb adlb <- dplyr::filter(adlb, PARAMCD == \"ALT\", AVISIT != \"SCREENING\") library(nestcolor) # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, table and customized confidence level g_lineplot( adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"), title = \"Plot of Mean and 80% Confidence Limits by Visit\" )"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"interactive-apps","dir":"Articles","previous_headings":"","what":"Interactive Apps","title":"Introduction to tern","text":"tern outputs easily accommodated shiny apps. recommend applying tern outputs teal apps. teal package shiny-based interactive exploration framework analyzing data. teal shiny apps tern outputs available teal.modules.clinical package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to tern","text":"summary, tern contains many additional functions creating tables, listing graphs used clinical trials statistical analyses. design package gives users lot flexibility meet analysis needs regulatory exploratory reporting context. information please explore tern website.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"tern-formatting-functions-overview","dir":"Articles","previous_headings":"","what":"tern Formatting Functions Overview","title":"Formatting Functions","text":"tern R package provides functions create common analyses clinical trials R functions default formatting arguments displaying values output specific way. tern formatting differs compared formatting available formatters package tern formats capable handling logical statements, allowing fine-tuning output displayed. Depending type value displayed, value , format output change. Whereas using formatters package, specified format applied regardless value. see available formatting functions available tern see ?formatting_functions. see available format strings available formatters see formatters::list_valid_format_labels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"comparing-tern-formatters-formats","dir":"Articles","previous_headings":"","what":"Comparing tern & formatters Formats","title":"Formatting Functions","text":"packages used vignette : example demonstrates use tern formatting count_abnormal() function. example “low” category non-zero numerator value fraction percentage value displayed, “high” value numerator value zero fraction value displayed without also displaying redundant zero percentage value. following example count_abnormal() function utilized . time “low” values “high” values non-zero numerator show percentage. following example demonstrates difference formatters used instead format output. choose use \"xx / xx\" value format. “high” value zero numerator value “low” value non-zero numerator, yet displayed format. concept occurs using available formats formatters package. following example displays result using \"xx.x / xx.x\" format instead. Use formatters::list_valid_format_labels() see full list available formats formatters.","code":"library(rtables) library(formatters) library(tern) library(dplyr) df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) #> high 0/2 df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) #> high 1/2 (50%) df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = \"xx / xx\") ) %>% build_table(df2) #> all obs #> —————————————— #> low 2 / 2 #> high 0 / 2 df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = \"xx.x / xx.x\") ) %>% build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 #> high 0.0 / 2.0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"formatting-function-basics","dir":"Articles","previous_headings":"","what":"Formatting Function Basics","title":"Formatting Functions","text":"Current tern formatting functions consider following aspects setting custom behaviors: Missing values - custom value string can set display missing values instead NA. 0’s - cell value zero, tern fraction formatting functions exclude accompanying percentage value. Number decimal places display - number decimal places can fixed needed. Value thresholds - different format value can displayed depending whether value within certain threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"number-of-decimal-places-to-display","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Number of Decimal Places to Display","title":"Formatting Functions","text":"Two functions set fixed number decimal places (specifically 1) format_fraction_fixed_dp() format_count_fraction_fixed_dp(). default, formatting functions remove trailing zeros, two functions always one decimal place percentage, even digit zero. See following example:","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 3L)) #> [1] \"1/3 (33.3%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.25)) #> [1] \"2 (25.0%)\""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"value-thresholds","dir":"Articles","previous_headings":"Formatting Function Basics","what":"Value Thresholds","title":"Formatting Functions","text":"Functions set custom values according certain threshold include format_extreme_values(), format_extreme_values_ci(), format_fraction_threshold(). extreme value formats work similarly allow user specify maximum number digits include, large small values given special string value. example: format_fraction_threshold() function allows user specify lower percentage threshold, values instead assigned special string value. example: See documentation function specific details behavior customize .","code":"extreme_format <- format_extreme_values(digits = 2) extreme_format(0.235) #> [1] \"0.23\" extreme_format(0.001) #> [1] \"<0.01\" extreme_format(Inf) #> [1] \">999.99\" fraction_format <- format_fraction_threshold(0.05) fraction_format(x = c(20, 0.1)) #> [1] 10 fraction_format(x = c(2, 0.01)) #> [1] \"<5\""},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"creating-custom-formatting-functions","dir":"Articles","previous_headings":"","what":"Creating Custom Formatting Functions","title":"Formatting Functions","text":"table requires customized output displayed using one pre-existing tern formatting functions, may want consider creating new formatting function. creating formatting function important consider aspects listed Formatting Function Customization section . section create custom formatting function derived format_fraction_fixed_dp() function. First take look function detail customize . see numerator value greater 0, fraction percentage displayed. numerator 0, fraction shown. Percent values always display 1 decimal place. create dummy dataset observe output value behavior formatting function applied. Now modify function make custom formatting function, custom_format. want display 3 decimal places percent value, numerator value 0 want display 0 value (without denominator).","code":"# First we will see how the format_fraction_fixed_dp code works and displays the outputs format_fraction_fixed_dp <- function(x, ...) { attr(x, \"label\") <- NULL checkmate::assert_vector(x) checkmate::assert_count(x[\"num\"]) checkmate::assert_count(x[\"denom\"]) result <- if (x[\"num\"] == 0) { paste0(x[\"num\"], \"/\", x[\"denom\"]) } else { paste0( x[\"num\"], \"/\", x[\"denom\"], \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" ) } return(result) } df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"LOW\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) #> high 0/2 custom_format <- function(x, ...) { attr(x, \"label\") <- NULL checkmate::assert_vector(x) checkmate::assert_count(x[\"num\"]) checkmate::assert_count(x[\"denom\"]) result <- if (x[\"num\"] == 0) { paste0(x[\"num\"]) # We remove the denominator on this line so that only a 0 is displayed } else { paste0( x[\"num\"], \"/\", x[\"denom\"], \" (\", sprintf(\"%.3f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" # We include 3 decimal places with %.3f ) } return(result) } basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\"), exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) #> high 0"},{"path":"https://insightsengineering.github.io/tern/main/articles/tern_formats.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Formatting Functions","text":"tern analysis function pre-specified default format functions implement generating output, taken formatters package custom formatting functions stored tern. tern functions differ compared formatters logical statements can used set value-dependent customized formats. like create custom formatting function use tern, sure carefully consider rules want implement handle different input values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joe Zhu. Author, maintainer. Daniel Sabanés Bové. Author. Jana Stoilova. Author. Davide Garolini. Author. Emily de la Rua. Author. Abinaya Yogasekaram. Author. Heng Wang. Author. Francois Collin. Author. Adrian Waddell. Author. Pawel Rucki. Author. Chendi Liao. Author. Jennifer Li. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/tern/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram , Wang H, Collin F, Waddell , Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used Clinical Trials. R package version 0.9.5.9026, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/main/.","code":"@Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, note = {R package version 0.9.5.9026, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/main/}, }"},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"tern-","dir":"","previous_headings":"","what":"Create Common TLGs Used in Clinical Trials","title":"Create Common TLGs Used in Clinical Trials","text":"tern R package contains analysis functions create tables graphs used clinical trial reporting. package provides large range functionality, : Data visualizations: Line plots (g_lineplot) Kaplan-Meier plots (g_km) Forest plots (g_forest) STEP graphs (g_step) Individual patient plots (g_ipp) Waterfall plots (g_waterfall) Statistical model fit summaries: Logistic regression (summarize_logistic) Cox regression (summarize_coxreg) Analysis tables: See list available analyze functions See list available summarize functions See list available column-wise analysis functions Many outputs available added teal shiny applications interactive exploration data. teal modules available teal.modules.clinical package. See TLG Catalog extensive catalog example clinical trial tables, listings, graphs created using tern functionality.","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Create Common TLGs Used in Clinical Trials","text":"tern available CRAN can install latest released version : can install latest development version directly GitHub running following:","code":"install.packages(\"tern\") # install.packages(\"pak\") pak::pak(\"insightsengineering/tern\")"},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Create Common TLGs Used in Clinical Trials","text":"understand use package, please refer Introduction tern article, provides multiple examples code implementation. See package vignettes browseVignettes(package = \"tern\") usage package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"related","dir":"","previous_headings":"","what":"Related","title":"Create Common TLGs Used in Clinical Trials","text":"rtables - table engine used tlg-catalog - website showcasing many examples clinical trial tables, listings, graphs teal.modules.clinical - teal modules interactive data analysis","code":""},{"path":"https://insightsengineering.github.io/tern/main/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"Create Common TLGs Used in Clinical Trials","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with abnormal range values — abnormal","title":"Count patients with abnormal range values — abnormal","text":"analyze function count_abnormal() creates layout element count patients abnormal analysis range values direction. function analyzes primary analysis variable var indicates abnormal range results. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline (defaults BNRIND), variable indicate baseline reference ranges. direction specified via abnormal parameter (e.g. High Low), fraction patient counts returned, numerator denominator calculated follows: num: number patients abnormality recorded treatment. denom: total number patients least one post-baseline assessment. function assumes df filtered include post-baseline records.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with abnormal range values — abnormal","text":"","code":"count_abnormal( lyt, var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = var, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal( df, .var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE ) a_count_abnormal( df, .var, abnormal = list(Low = \"LOW\", High = \"HIGH\"), variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), exclude_base_abn = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with abnormal range values — abnormal","text":"lyt (PreDataTableLayouts) layout analyses added . abnormal (named list) list identifying abnormal range level(s) var. Defaults list(Low = \"LOW\", High = \"HIGH\") can also group different levels named list, example, abnormal = list(Low = c(\"LOW\", \"LOW LOW\"), High = c(\"HIGH\", \"HIGH HIGH\")). variables (named list string) list additional analysis variables. exclude_base_abn (flag) whether exclude subjects baseline abnormality numerator denominator. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal() table layout. s_count_abnormal() returns statistic fraction vector num denom counts patients. a_count_abnormal() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal(): Statistics function counts patients abnormal range values single abnormal level. a_count_abnormal(): Formatted analysis function used afun count_abnormal().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with abnormal range values — abnormal","text":"count_abnormal() considers single variable contains multiple abnormal levels. df filtered include post-baseline records. denominator includes patients may abnormal levels baseline, patients missing baseline records. Patients abnormalities baseline can optionally excluded numerator denominator via exclude_base_abn parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with abnormal range values — abnormal","text":"","code":"library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #> #> matches #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union df <- data.frame( USUBJID = as.character(c(1, 1, 2, 2)), ANRIND = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BNRIND = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") # Layout creating function. basic_table() %>% count_abnormal(var = \"ANRIND\", abnormal = list(high = \"HIGH\", low = \"LOW\")) %>% build_table(df) #> all obs #> ———————————————— #> high 1/2 (50%) #> low 1/2 (50%) # Passing of statistics function and formatting arguments. df2 <- data.frame( ID = as.character(c(1, 1, 2, 2)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BL_RANGE = factor(c(\"NORMAL\", \"NORMAL\", \"HIGH\", \"HIGH\")), ONTRTFL = c(\"\", \"Y\", \"\", \"Y\"), stringsAsFactors = FALSE ) # Select only post-baseline records. df2 <- df2 %>% filter(ONTRTFL == \"Y\") basic_table() %>% count_abnormal( var = \"RANGE\", abnormal = list(low = \"LOW\", high = \"HIGH\"), variables = list(id = \"ID\", baseline = \"BL_RANGE\") ) %>% build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) #> high 1/2 (50%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"analyze function count_abnormal_by_baseline() creates layout element count patients abnormal analysis range values, categorized baseline status. function analyzes primary analysis variable var indicates abnormal range results. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline (defaults BNRIND), variable indicate baseline reference ranges. direction specified via abnormal parameter (e.g. High Low), condition baseline range result count patients numerator denominator follows following categories: num: number patients without abnormality baseline (excluding missing baseline) least one abnormality post-baseline. denom: number patients without abnormality baseline (excluding missing baseline). num: number patients abnormality baseline least one abnormality post-baseline. denom: number patients abnormality baseline. Total num: number patients least one post-baseline record least one abnormality post-baseline. denom: number patients least one post-baseline record. function assumes df filtered include post-baseline records.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"","code":"count_abnormal_by_baseline( lyt, var, abnormal, variables = list(id = \"USUBJID\", baseline = \"BNRIND\"), na_str = \"\", nested = TRUE, ..., table_names = abnormal, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_baseline( df, .var, abnormal, na_str = \"\", variables = list(id = \"USUBJID\", baseline = \"BNRIND\") ) a_count_abnormal_by_baseline( df, .var, abnormal, na_str = \"\", variables = list(id = \"USUBJID\", baseline = \"BNRIND\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"lyt (PreDataTableLayouts) layout analyses added . abnormal (character) values identifying abnormal range level(s) .var. variables (named list string) list additional analysis variables. na_str (string) explicit na_level argument used pre-processing steps (maybe df_explicit_na()). default \"\". nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal_by_baseline\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"count_abnormal_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_baseline() table layout. s_count_abnormal_by_baseline() returns statistic fraction named list 3 labeled elements: not_abnormal, abnormal, total. element contains vector num denom patient counts. a_count_abnormal_by_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"count_abnormal_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function used afun count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"df filtered include post-baseline records. baseline variable analysis variable contains NA records, expected df pre-processed using df_explicit_na() explicit_na().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with abnormal analysis range values by baseline status — abnormal_by_baseline","text":"","code":"df <- data.frame( USUBJID = as.character(c(1:6)), ANRIND = factor(c(rep(\"LOW\", 4), \"NORMAL\", \"HIGH\")), BNRIND = factor(c(\"LOW\", \"NORMAL\", \"HIGH\", NA, \"LOW\", \"NORMAL\")) ) df <- df_explicit_na(df) # Layout creating function. basic_table() %>% count_abnormal_by_baseline(var = \"ANRIND\", abnormal = c(High = \"HIGH\")) %>% build_table(df) #> all obs #> ———————————————————————— #> High #> Not high 1/4 (25%) #> High 0/1 #> Total 1/6 (16.7%) # Passing of statistics function and formatting arguments. df2 <- data.frame( ID = as.character(c(1, 2, 3, 4)), RANGE = factor(c(\"NORMAL\", \"LOW\", \"HIGH\", \"HIGH\")), BLRANGE = factor(c(\"LOW\", \"HIGH\", \"HIGH\", \"NORMAL\")) ) basic_table() %>% count_abnormal_by_baseline( var = \"RANGE\", abnormal = c(Low = \"LOW\"), variables = list(id = \"ID\", baseline = \"BLRANGE\"), .formats = c(fraction = \"xx / xx\"), .indent_mods = c(fraction = 2L) ) %>% build_table(df2) #> all obs #> ——————————————————————— #> Low #> Not low 1 / 3 #> Low 0 / 1 #> Total 1 / 4"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with marked laboratory abnormalities — abnormal_by_marked","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"analyze function count_abnormal_by_marked() creates layout element count patients marked laboratory abnormalities direction abnormality, categorized parameter value. function analyzes primary analysis variable var indicates whether single, replicated, last marked laboratory abnormality observed. Levels var include marked lab abnormality (single last_replicated) can supplied via category parameter. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, param (defaults PARAM), variable indicate parameter values, direction (defaults abn_dir), variable indicate abnormality directions. combination param direction levels, marked lab abnormality counts calculated follows: Single, last & Last replicated: number patients Single, last Last replicated values, respectively. : number patients either single replicated marked abnormalities. Fractions calculated dividing counts number patients least one valid measurement recorded analysis. Prior using function table layout must use rtables::split_rows_by() create two row splits, one variable param one variable direction.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"","code":"count_abnormal_by_marked( lyt, var, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\"), na_str = default_na_str(), nested = TRUE, ..., .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_marked( df, .var = \"AVALCAT1\", .spl_context, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") ) a_count_abnormal_by_marked( df, .var = \"AVALCAT1\", .spl_context, category = list(single = \"SINGLE\", last_replicated = c(\"LAST\", \"REPLICATED\")), variables = list(id = \"USUBJID\", param = \"PARAM\", direction = \"abn_dir\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"lyt (PreDataTableLayouts) layout analyses added . category (list) list different marked category names single last replicated. variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"abnormal_by_marked\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"count_abnormal_by_marked() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_marked() table layout. s_count_abnormal_by_marked() returns statistic count_fraction Single, last, Last replicated, results. a_count_abnormal_by_marked() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"count_abnormal_by_marked(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_marked(): Statistics function patients marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function used afun count_abnormal_by_marked().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"Single, last Last replicated levels mutually exclusive. patient abnormalities meet Single, last Last replicated criteria, patient counted Last replicated category.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_marked.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with marked laboratory abnormalities — abnormal_by_marked","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))), ARMCD = factor(c(rep(\"ARM A\", 5), rep(\"ARM B\", 5), rep(\"ARM A\", 5), rep(\"ARM B\", 5))), ANRIND = factor(c( \"NORMAL\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"HIGH\", \"HIGH\", \"HIGH\", \"HIGH HIGH\", \"NORMAL\", \"HIGH HIGH\", \"NORMAL\", \"LOW\", \"LOW\", \"LOW LOW\", \"LOW\", \"LOW\", \"LOW\", \"LOW LOW\", \"NORMAL\", \"LOW LOW\" )), ONTRTFL = rep(c(\"\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\", \"Y\"), 2), PARAMCD = factor(c(rep(\"CRP\", 10), rep(\"ALT\", 10))), AVALCAT1 = factor(rep(c(\"\", \"\", \"\", \"SINGLE\", \"REPLICATED\", \"\", \"\", \"LAST\", \"\", \"SINGLE\"), 2)), stringsAsFactors = FALSE ) df <- df %>% mutate(abn_dir = factor( case_when( ANRIND == \"LOW LOW\" ~ \"Low\", ANRIND == \"HIGH HIGH\" ~ \"High\", TRUE ~ \"\" ), levels = c(\"Low\", \"High\") )) # Select only post-baseline records. df <- df %>% filter(ONTRTFL == \"Y\") df_crp <- df %>% filter(PARAMCD == \"CRP\") %>% droplevels() full_parent_df <- list(df_crp, \"not_needed\") cur_col_subset <- list(rep(TRUE, nrow(df_crp)), \"not_needed\") spl_context <- data.frame( split = c(\"PARAMCD\", \"GRADE_DIR\"), full_parent_df = I(full_parent_df), cur_col_subset = I(cur_col_subset) ) map <- unique( df[df$abn_dir %in% c(\"Low\", \"High\") & df$AVALCAT1 != \"\", c(\"PARAMCD\", \"abn_dir\")] ) %>% lapply(as.character) %>% as.data.frame() %>% arrange(PARAMCD, abn_dir) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAMCD\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\" ) %>% split_rows_by( \"abn_dir\", split_fun = trim_levels_to_map(map) ) %>% count_abnormal_by_marked( var = \"AVALCAT1\", variables = list( id = \"USUBJID\", param = \"PARAMCD\", direction = \"abn_dir\" ) ) %>% build_table(df = df) #> ARM A ARM B #> ———————————————————————————————————————————— #> ALT (n) 1 1 #> Low #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) #> CRP (n) 1 1 #> High #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAMCD\") %>% summarize_num_patients( var = \"USUBJID\", .stats = \"unique_count\" ) %>% split_rows_by( \"abn_dir\", split_fun = trim_levels_in_group(\"abn_dir\") ) %>% count_abnormal_by_marked( var = \"AVALCAT1\", variables = list( id = \"USUBJID\", param = \"PARAMCD\", direction = \"abn_dir\" ) ) %>% build_table(df = df) #> ARM A ARM B #> ———————————————————————————————————————————— #> ALT (n) 1 1 #> Low #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%) #> CRP (n) 1 1 #> High #> Single, not last 1 (100%) 0 #> Last or replicated 0 1 (100%) #> Any Abnormality 1 (100%) 1 (100%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"analyze function count_abnormal_by_worst_grade() creates layout element count patients highest (worst) analysis toxicity grade post-baseline direction, categorized parameter value. function analyzes primary analysis variable var indicates toxicity grades. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, param (defaults PARAM), variable indicate parameter values, grade_dir (defaults GRADE_DIR), variable indicate directions (e.g. High Low) toxicity grade supplied var. combination param grade_dir levels, patient counts worst grade calculated follows: 1 4: number patients worst grades 1-4, respectively. : number patients least one abnormality (.e. grade 0). Fractions calculated dividing counts number patients least one valid measurement recorded treatment. Pre-processing crucial using function can done automatically using h_adlb_abnormal_by_worst_grade() helper function. See description function details necessary pre-processing steps. Prior using function table layout must use rtables::split_rows_by() create two row splits, one variable param one variable grade_dir.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"","code":"count_abnormal_by_worst_grade( lyt, var, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\"), na_str = default_na_str(), nested = TRUE, ..., .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_by_worst_grade( df, .var = \"GRADE_ANL\", .spl_context, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") ) a_count_abnormal_by_worst_grade( df, .var = \"GRADE_ANL\", .spl_context, variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"abnormal_by_worst_grade\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .spl_context (data.frame) gives information ancestor split states passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"count_abnormal_by_worst_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_by_worst_grade() table layout. s_count_abnormal_by_worst_grade() returns single statistic count_fraction grades 1 4 \"\" results. a_count_abnormal_by_worst_grade() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"count_abnormal_by_worst_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function counts patients worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function used afun count_abnormal_by_worst_grade().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients by most extreme post-baseline toxicity grade per direction of abnormality — abnormal_by_worst_grade","text":"","code":"library(dplyr) library(forcats) adlb <- tern_ex_adlb # Data is modified in order to have some parameters with grades only in one direction # and simulate the real data. adlb$ATOXGR[adlb$PARAMCD == \"ALT\" & adlb$ATOXGR %in% c(\"1\", \"2\", \"3\", \"4\")] <- \"-1\" adlb$ANRIND[adlb$PARAMCD == \"ALT\" & adlb$ANRIND == \"HIGH\"] <- \"LOW\" adlb$WGRHIFL[adlb$PARAMCD == \"ALT\"] <- \"\" adlb$ATOXGR[adlb$PARAMCD == \"IGA\" & adlb$ATOXGR %in% c(\"-1\", \"-2\", \"-3\", \"-4\")] <- \"1\" adlb$ANRIND[adlb$PARAMCD == \"IGA\" & adlb$ANRIND == \"LOW\"] <- \"HIGH\" adlb$WGRLOFL[adlb$PARAMCD == \"IGA\"] <- \"\" # Pre-processing adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade() # Map excludes records without abnormal grade since they should not be displayed # in the table. map <- unique(adlb_f[adlb_f$GRADE_DIR != \"ZERO\", c(\"PARAM\", \"GRADE_DIR\", \"GRADE_ANL\")]) %>% lapply(as.character) %>% as.data.frame() %>% arrange(PARAM, desc(GRADE_DIR), GRADE_ANL) basic_table() %>% split_cols_by(\"ARMCD\") %>% split_rows_by(\"PARAM\") %>% split_rows_by(\"GRADE_DIR\", split_fun = trim_levels_to_map(map)) %>% count_abnormal_by_worst_grade( var = \"GRADE_ANL\", variables = list(id = \"USUBJID\", param = \"PARAM\", grade_dir = \"GRADE_DIR\") ) %>% build_table(df = adlb_f) #> ARM A ARM B ARM C #> ——————————————————————————————————————————————————————————————————————————— #> Alanine Aminotransferase Measurement #> LOW #> 1 12 (17.4%) 5 (6.8%) 8 (13.8%) #> 2 9 (13%) 13 (17.8%) 6 (10.3%) #> 3 6 (8.7%) 4 (5.5%) 6 (10.3%) #> 4 7 (10.1%) 7 (9.6%) 6 (10.3%) #> Any 34 (49.3%) 29 (39.7%) 26 (44.8%) #> C-Reactive Protein Measurement #> LOW #> 1 11 (15.9%) 12 (16.4%) 7 (12.1%) #> 2 8 (11.6%) 2 (2.7%) 6 (10.3%) #> 3 4 (5.8%) 9 (12.3%) 6 (10.3%) #> 4 7 (10.1%) 6 (8.2%) 4 (6.9%) #> Any 30 (43.5%) 29 (39.7%) 23 (39.7%) #> HIGH #> 1 8 (11.6%) 11 (15.1%) 2 (3.4%) #> 2 9 (13%) 11 (15.1%) 11 (19%) #> 3 14 (20.3%) 10 (13.7%) 5 (8.6%) #> 4 2 (2.9%) 4 (5.5%) 6 (10.3%) #> Any 33 (47.8%) 36 (49.3%) 24 (41.4%) #> Immunoglobulin A Measurement #> HIGH #> 1 7 (10.1%) 7 (9.6%) 6 (10.3%) #> 2 8 (11.6%) 6 (8.2%) 8 (13.8%) #> 3 7 (10.1%) 5 (6.8%) 9 (15.5%) #> 4 6 (8.7%) 2 (2.7%) 3 (5.2%) #> Any 28 (40.6%) 20 (27.4%) 26 (44.8%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"analyze function count_abnormal_lab_worsen_by_baseline() creates layout element count patients analysis toxicity grades worsened baseline, categorized highest (worst) grade post-baseline. function analyzes primary analysis variable var indicates analysis toxicity grades. Additional analysis variables can supplied list via variables parameter id (defaults USUBJID), variable indicate unique subject identifiers, baseline_var (defaults BTOXGR), variable indicate baseline toxicity grades, direction_var (defaults GRADDIR), variable indicate toxicity grade directions interest include (e.g. \"H\" (high), \"L\" (low), \"B\" ()). direction(s) specified direction_var, patient counts worst grade patients worsened baseline calculated follows: 1 4: number patients worsened baseline grades worst grades 1-4, respectively. : total number patients worsened baseline grades. Fractions calculated dividing counts number patients analysis toxicity grades worsened baseline toxicity grades treatment. Prior using function table layout must use rtables::split_rows_by() create row split variable direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"","code":"count_abnormal_lab_worsen_by_baseline( lyt, var, variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\"), na_str = default_na_str(), nested = TRUE, ..., table_names = NULL, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_abnormal_lab_worsen_by_baseline( df, .var = \"ATOXGR\", variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") ) a_count_abnormal_lab_worsen_by_baseline( df, .var = \"ATOXGR\", variables = list(id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables including: id (string) subject variable name. baseline_var (string) name data column containing baseline toxicity variable. direction_var (string) see direction_var details. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"abnormal_by_worst_grade_worsen\") see available statistics. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"count_abnormal_lab_worsen_by_baseline() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_abnormal_lab_worsen_by_baseline() table layout. s_count_abnormal_lab_worsen_by_baseline() returns counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\". a_count_abnormal_lab_worsen_by_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"count_abnormal_lab_worsen_by_baseline(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function patients whose worst post-baseline lab grades worse baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function used afun count_abnormal_lab_worsen_by_baseline().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/abnormal_by_worst_grade_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline — abnormal_by_worst_grade_worsen","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" ) basic_table() %>% split_cols_by(\"ARMCD\") %>% add_colcounts() %>% split_rows_by(\"PARAMCD\") %>% split_rows_by(\"GRADDR\") %>% count_abnormal_lab_worsen_by_baseline( var = \"ATOXGR\", variables = list( id = \"USUBJID\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\" ) ) %>% append_topleft(\"Direction of Abnormality\") %>% build_table(df = df, alt_counts_df = tern_ex_adsl) #> ARM A ARM B ARM C #> Direction of Abnormality (N=69) (N=73) (N=58) #> ———————————————————————————————————————————————————————————————————————— #> IGA #> High #> 1 6/63 (9.5%) 6/64 (9.4%) 4/50 (8%) #> 2 8/64 (12.5%) 5/67 (7.5%) 8/53 (15.1%) #> 3 7/66 (10.6%) 5/68 (7.4%) 9/57 (15.8%) #> 4 6/68 (8.8%) 2/72 (2.8%) 3/58 (5.2%) #> Any 27/68 (39.7%) 18/72 (25%) 24/58 (41.4%) #> ALT #> High #> 1 7/63 (11.1%) 6/62 (9.7%) 2/48 (4.2%) #> 2 12/63 (19%) 4/67 (6%) 11/50 (22%) #> 3 4/65 (6.2%) 11/71 (15.5%) 7/56 (12.5%) #> 4 1/67 (1.5%) 8/71 (11.3%) 4/57 (7%) #> Any 24/67 (35.8%) 29/71 (40.8%) 24/57 (42.1%) #> Low #> 1 12/67 (17.9%) 4/66 (6.1%) 7/52 (13.5%) #> 2 9/68 (13.2%) 12/69 (17.4%) 6/55 (10.9%) #> 3 6/69 (8.7%) 4/71 (5.6%) 5/56 (8.9%) #> 4 7/69 (10.1%) 7/73 (9.6%) 6/58 (10.3%) #> Any 34/69 (49.3%) 27/73 (37%) 24/58 (41.4%) #> CRP #> Low #> 1 11/66 (16.7%) 10/67 (14.9%) 4/47 (8.5%) #> 2 8/66 (12.1%) 1/70 (1.4%) 6/50 (12%) #> 3 4/68 (5.9%) 9/70 (12.9%) 5/53 (9.4%) #> 4 7/69 (10.1%) 6/72 (8.3%) 4/55 (7.3%) #> Any 30/69 (43.5%) 26/72 (36.1%) 19/55 (34.5%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Split function to configure risk difference column — add_riskdiff","title":"Split function to configure risk difference column — add_riskdiff","text":"Wrapper function rtables::add_combo_levels() configures settings risk difference column added rtables object. add risk difference column table, function used split_fun calls rtables::split_cols_by(), followed setting argument riskdiff TRUE following analyze function calls.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split function to configure risk difference column — add_riskdiff","text":"","code":"add_riskdiff( arm_x, arm_y, col_label = paste0(\"Risk Difference (%) (95% CI)\", if (length(arm_y) > 1) paste0(\"\\n\", arm_x, \" vs. \", arm_y)), pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split function to configure risk difference column — add_riskdiff","text":"arm_x (string) name reference arm use risk difference calculations. arm_y (character) names one arms compare reference arm risk difference calculations. new column added value arm_y. col_label (character) labels use rendering risk difference column within table. one comparison arm specified arm_y, default labels specify two arms compared (reference arm vs. comparison arm). pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split function to configure risk difference column — add_riskdiff","text":"closure suitable use split function (split_fun) within rtables::split_cols_by() creating table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/add_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split function to configure risk difference column — add_riskdiff","text":"","code":"adae <- tern_ex_adae adae$AESEV <- factor(adae$AESEV) lyt <- basic_table() %>% split_cols_by(\"ARMCD\", split_fun = add_riskdiff(arm_x = \"ARM A\", arm_y = c(\"ARM B\", \"ARM C\"))) %>% count_occurrences_by_grade( var = \"AESEV\", riskdiff = TRUE ) tbl <- build_table(lyt, df = adae) tbl #> Risk Difference (%) (95% CI) Risk Difference (%) (95% CI) #> ARM A ARM B ARM C ARM A vs. ARM B ARM A vs. ARM C #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> MILD 6 (3.0%) 4 (2.3%) 2 (1.2%) 0.7 (-2.5 - 3.9) 1.7 (-1.2 - 4.6) #> MODERATE 19 (9.4%) 15 (8.5%) 14 (8.6%) 0.9 (-4.8 - 6.7) 0.8 (-5.1 - 6.7) #> SEVERE 34 (16.8%) 38 (21.5%) 32 (19.8%) -4.6 (-12.6 - 3.3) -2.9 (-10.9 - 5.1)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout-creating function to add row total counts — add_rowcounts","title":"Layout-creating function to add row total counts — add_rowcounts","text":"works analogously rtables::add_colcounts() rows. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout-creating function to add row total counts — add_rowcounts","text":"","code":"add_rowcounts(lyt, alt_counts = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout-creating function to add row total counts — add_rowcounts","text":"lyt (PreDataTableLayouts) layout analyses added . alt_counts (flag) whether row counts taken alt_counts_df (TRUE) df (FALSE). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout-creating function to add row total counts — add_rowcounts","text":"modified layout latest row split labels now row-wise total counts (.e. without column-based subsetting) attached parentheses.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout-creating function to add row total counts — add_rowcounts","text":"Row count values contained row count rows displayed considered zero rows default pruning.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/add_rowcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout-creating function to add row total counts — add_rowcounts","text":"","code":"basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% add_rowcounts() %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> —————————————————————————————————————————————————————————————————————————— #> ASIAN (N=231) #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 28.00 30.00 #> Median 33.00 32.50 33.50 #> Mean 34.20 32.68 34.63 #> 3rd Qu. 38.50 36.00 38.00 #> Max. 58.00 55.00 53.00 #> BLACK OR AFRICAN AMERICAN (N=79) #> Min. 23.00 21.00 24.00 #> 1st Qu. 29.00 28.75 29.00 #> Median 33.00 30.00 32.00 #> Mean 34.68 31.71 34.00 #> 3rd Qu. 37.25 36.25 39.00 #> Max. 60.00 42.00 51.00 #> WHITE (N=46) #> Min. 30.00 25.00 28.00 #> 1st Qu. 38.00 31.00 30.25 #> Median 40.50 37.50 35.00 #> Mean 39.36 36.93 35.11 #> 3rd Qu. 43.50 40.00 37.50 #> Max. 47.00 55.00 47.00"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for adverse event baskets — aesi_label","text":"","code":"aesi_label(aesi, scope = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for adverse event baskets — aesi_label","text":"aesi (character) vector standardized MedDRA query name (e.g. SMQxxNAM) customized query name (e.g. CQxxNAM). scope (character) vector scope query (e.g. SMQxxSC).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for adverse event baskets — aesi_label","text":"string standard label AE basket.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/aesi_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Labels for adverse event baskets — aesi_label","text":"","code":"adae <- tern_ex_adae # Standardized query label includes scope. aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC) #> [1] \"C.1.1.1.3/B.2.2.3.1 aesi (BROAD)\" # Customized query label. aesi_label(adae$CQ01NAM) #> [1] \"D.2.1.5.3/A.1.1.1.1 aesi\""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis function to calculate risk difference column values — afun_riskdiff","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"risk difference column, function uses statistics function associated afun calculates risk difference values arm X (reference group) arm Y. arms specified configuring risk difference column done using add_riskdiff() split function previous call rtables::split_cols_by(). columns, applies afun usual. function utilizes stat_propdiff_ci() function perform risk difference calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"","code":"afun_riskdiff( df, labelstr = \"\", .var, .N_col, .N_row, .df_row, .spl_context, .all_col_counts, .stats, .formats = NULL, .labels = NULL, .indent_mods = NULL, na_str = default_na_str(), afun, s_args = list() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .var (string) single variable name passed rtables requested statistics function. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .df_row (data.frame) data frame across columns given row split. .spl_context (data.frame) gives information ancestor split states passed rtables. .all_col_counts (integer) vector value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_str (string) string used replace NA empty values output. afun (named list) named list containing one name-value pair name corresponds name statistics function used calculations value corresponding analysis function. s_args (named list) additional arguments passed statistics function analysis function supplied afun.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis function to calculate risk difference column values — afun_riskdiff","text":"list formatted rtables::CellValue().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get selected statistics names — afun_selected_stats","title":"Get selected statistics names — afun_selected_stats","text":"Helper function used creating afun.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get selected statistics names — afun_selected_stats","text":"","code":"afun_selected_stats(.stats, all_stats)"},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get selected statistics names — afun_selected_stats","text":".stats (vector NULL) input layout creating function. Note NULL means context default statistics used. all_stats (character) statistics can selected potentially.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/afun_selected_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get selected statistics names — afun_selected_stats","text":"character vector selected statistics.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_colvars_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze functions in columns — analyze_colvars_functions","title":"Analyze functions in columns — analyze_colvars_functions","text":"functions wrappers rtables::analyze_colvars() apply corresponding tern statistics functions add analysis given table layout. particular, functions designed analysis methods split different columns. analyze_vars_in_cols(): fundamental tabulation analysis methods onto columns. words, analysis methods defined column space, .e. become column labels. changing variable vector, list functions can applied different variables, caveat number statistical functions. tabulate_rsp_subgroups(): similarly analyze_vars_in_cols, function combines analyze_colvars summarize_row_groups compact way produce standard tables show analysis methods columns. tabulate_survival_subgroups(): function similar , used tables. analyze_patients_exposure_in_cols(): based analyze_colvars. needs summarize_patients_exposure_in_cols() leverage nesting label rows analysis rtables::summarize_row_groups(). summarize_coxreg(): generally based rtables::summarize_row_groups(), behaves similarly tabulate_* functions described designed provide specific standard tables may contain nested structure combination summarize_row_groups() rtables::analyze_colvars().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze functions — analyze_functions","title":"Analyze functions — analyze_functions","text":"functions wrappers rtables::analyze() apply corresponding tern statistics functions add analysis given table layout: analyze_num_patients() analyze_vars() compare_vars() count_abnormal() count_abnormal_by_baseline() count_abnormal_by_marked() count_abnormal_by_worst_grade() count_cumulative() count_missed_doses() count_occurrences() count_occurrences_by_grade() count_patients_events_in_cols() count_patients_with_event() count_patients_with_flags() count_values() coxph_pairwise() estimate_incidence_rate() estimate_multinomial_rsp() estimate_odds_ratio() estimate_proportion() estimate_proportion_diff() summarize_ancova() summarize_colvars(): even function uses rtables::analyze_colvars(), applies analysis methods different rows one variables split different columns. comparison, analyze_colvars_functions leverage analyze_colvars context split rows analysis methods columns. summarize_change() surv_time() surv_timepoint() test_proportion_diff()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze variables — analyze_variables","title":"Analyze variables — analyze_variables","text":"analyze function analyze_vars() creates layout element summarize one variables, using S3 generic function s_summary() calculate list summary statistics. list available statistics numeric variables can viewed running get_stats(\"analyze_vars_numeric\") non-numeric variables running get_stats(\"analyze_vars_counts\"). Use .stats parameter specify statistics include output summary table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze variables — analyze_variables","text":"","code":"analyze_vars( lyt, vars, var_labels = vars, na_str = default_na_str(), nested = TRUE, ..., na.rm = TRUE, show_labels = \"default\", table_names = vars, section_div = NA_character_, .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...) # S3 method for class 'numeric' s_summary( x, na.rm = TRUE, denom, .N_row, .N_col, .var, control = control_analyze_vars(), ... ) # S3 method for class 'factor' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, ... ) # S3 method for class 'character' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, .var, verbose = TRUE, ... ) # S3 method for class 'logical' s_summary( x, na.rm = TRUE, denom = c(\"n\", \"N_row\", \"N_col\"), .N_row, .N_col, ... ) a_summary( x, .N_col, .N_row, .var = NULL, .df_row = NULL, .ref_group = NULL, .in_ref_col = FALSE, compare = FALSE, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, na.rm = TRUE, na_str = default_na_str(), ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze variables — analyze_variables","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_summary(). na.rm (flag) whether NA values removed x prior analysis. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric\") see statistics available numeric variables, get_stats(\"analyze_vars_counts\") statistics available non-numeric variables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. x (numeric) vector numbers want analyze. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .var (string) single variable name passed rtables requested statistics function. control (list) parameters descriptive statistics details, specified using helper function control_analyze_vars(). possible parameter options : conf_level (proportion) confidence level interval mean median. quantiles (numeric(2)) vector length two specify quantiles. quantile_type (numeric(1)) 1 9 selecting quantile algorithms used. See type stats::quantile(). test_mean (numeric(1)) value test mean null hypothesis calculating p-value. verbose (flag) defaults TRUE, prints warnings messages. mainly used print information factor casting. .df_row (data.frame) data frame across columns given row split. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. compare (flag) whether comparison statistics analyzed instead summary statistics (compare = TRUE adds pval statistic comparing reference group).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analyze variables — analyze_variables","text":"analyze_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_summary() table layout. s_summary() returns different statistics depending class x. x class numeric, returns list following named numeric items: n: length() x. sum: sum() x. mean: mean() x. sd: stats::sd() x. se: standard error x mean, .e.: (sd(x) / sqrt(length(x))). mean_sd: mean() stats::sd() x. mean_se: mean() x standard error (see ). mean_ci: CI mean x (stat_mean_ci()). mean_sei: SE interval mean x, .e.: (mean() -/+ stats::sd() / sqrt()). mean_sdi: SD interval mean x, .e.: (mean() -/+ stats::sd()). mean_pval: two-sided p-value mean x (stat_mean_pval()). median: stats::median() x. mad: median absolute deviation x, .e.: (stats::median() xc, xc = x - stats::median()). median_ci: CI median x (stat_median_ci()). quantiles: Two sample quantiles x (stats::quantile()). iqr: stats::IQR() x. range: range_noinf() x. min: max() x. max: min() x. median_range: median() range_noinf() x. cv: coefficient variation x, .e.: (stats::sd() / mean() * 100). geom_mean: geometric mean x, .e.: (exp(mean(log(x)))). geom_cv: geometric coefficient variation x, .e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100). x class factor converted character, returns list named numeric items: n: length() x. count: list number cases level factor x. count_fraction: Similar count also includes proportion cases level factor x relative denominator, NA denominator zero. x class logical, returns list named numeric items: n: length() x (possibly removing NAs). count: Count TRUE x. count_fraction: Count proportion TRUE x relative denominator, NA denominator zero. Note NAs x never counted leading NA . a_summary() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyze variables — analyze_variables","text":"Automatic digit formatting: number digits display can automatically determined analyzed variable(s) (vars) certain statistics setting statistic format \"auto\" .formats. utilizes format_auto() formatting function. Note data current row & variable (columns) considered (.df_row[[.var]], see rtables::additional_fun_params) whole dataset.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Analyze variables — analyze_variables","text":"analyze_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_summary(): S3 generic function produces variable summary. s_summary(numeric): Method numeric class. s_summary(factor): Method factor class. s_summary(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_summary(logical): Method logical class. a_summary(): Formatted analysis function used afun analyze_vars() compare_vars() cfun summarize_colvars().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Analyze variables — analyze_variables","text":"x empty vector, NA returned. expected feature return rcell content rtables intersection column row delimits empty data selection. mean function applied empty vector, NA returned instead NaN, latter standard behavior R. x empty factor, list still returned counts one element per factor level. levels x, function fails. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"\") also excluded na.rm set TRUE. Automatic conversion character factor guarantee table can generated correctly. particular sparse tables likely can fail. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table(). use comparison (additional p-value statistic), parameter compare must set TRUE. Ensure either NA values converted explicit NA level NA values left .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyze variables — analyze_variables","text":"","code":"## Fabricated dataset. dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)) ) # `analyze_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\") build_table(l, df = dta_test) #> A B C #> ———————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean (SD) 7.5 (2.1) 3.0 (NA) NA #> Median 7.5 3.0 NA #> Min - Max 6.0 - 9.0 3.0 - 3.0 NA #> V2 #> n 2 1 0 #> Mean (SD) 6.5 (2.1) 2.0 (NA) NA #> Median 6.5 2.0 NA #> Min - Max 5.0 - 8.0 2.0 - 2.0 NA #> V3 #> n 2 1 0 #> Mean (SD) 5.5 (2.1) 1.0 (NA) NA #> Median 5.5 1.0 NA #> Min - Max 4.0 - 7.0 1.0 - 1.0 NA ## Select and format statistics output. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars( vars = \"AVAL\", .stats = c(\"n\", \"mean_sd\", \"quantiles\"), .formats = c(\"mean_sd\" = \"xx.x, xx.x\"), .labels = c(n = \"n\", mean_sd = \"Mean, SD\", quantiles = c(\"Q1 - Q3\")) ) build_table(l, df = dta_test) #> A B C #> ——————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean, SD 7.5, 2.1 3.0, NA NA #> Q1 - Q3 6.0 - 9.0 3.0 - 3.0 NA #> V2 #> n 2 1 0 #> Mean, SD 6.5, 2.1 2.0, NA NA #> Q1 - Q3 5.0 - 8.0 2.0 - 2.0 NA #> V3 #> n 2 1 0 #> Mean, SD 5.5, 2.1 1.0, NA NA #> Q1 - Q3 4.0 - 7.0 1.0 - 1.0 NA ## Use arguments interpreted by `s_summary`. l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\", na.rm = FALSE) build_table(l, df = dta_test) #> A B C #> ————————————————————————————————— #> V1 #> n 2 2 2 #> Mean (SD) 7.5 (2.1) NA NA #> Median 7.5 NA NA #> Min - Max 6.0 - 9.0 NA NA #> V2 #> n 2 2 2 #> Mean (SD) 6.5 (2.1) NA NA #> Median 6.5 NA NA #> Min - Max 5.0 - 8.0 NA NA #> V3 #> n 2 2 2 #> Mean (SD) 5.5 (2.1) NA NA #> Median 5.5 NA NA #> Min - Max 4.0 - 7.0 NA NA ## Handle `NA` levels first when summarizing factors. dta_test$AVISIT <- NA_character_ dta_test <- df_explicit_na(dta_test) l <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze_vars(vars = \"AVISIT\", na.rm = FALSE) build_table(l, df = dta_test) #> A B C #> —————————————————————————————————————————— #> n 6 6 6 #> 6 (100%) 6 (100%) 6 (100%) # auto format dt <- data.frame(\"VAR\" = c(0.001, 0.2, 0.0011000, 3, 4)) basic_table() %>% analyze_vars( vars = \"VAR\", .stats = c(\"n\", \"mean\", \"mean_sd\", \"range\"), .formats = c(\"mean_sd\" = \"auto\", \"range\" = \"auto\") ) %>% build_table(dt) #> all obs #> ————————————————————————————— #> n 5 #> Mean 1.4 #> Mean (SD) 1.44042 (1.91481) #> Min - Max 0.0010 - 4.0000 # `s_summary.numeric` ## Basic usage: empty numeric returns NA-filled items. s_summary(numeric()) #> $n #> n #> 0 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NaN #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> ## Management of NA values. x <- c(NA_real_, 1) s_summary(x, na.rm = TRUE) #> $n #> n #> 1 #> #> $sum #> sum #> 1 #> #> $mean #> mean #> 1 #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> 1 NA #> #> $mean_se #> mean se #> 1 NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 1 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 1 1 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 0 #> #> $range #> min max #> 1 1 #> #> $min #> min #> 1 #> #> $max #> max #> 1 #> #> $median_range #> median min max #> 1 1 1 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> 1 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> s_summary(x, na.rm = FALSE) #> $n #> n #> 2 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NA #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> x <- c(NA_real_, 1, 2) s_summary(x, stats = NULL) #> $n #> n #> 2 #> #> $sum #> sum #> 3 #> #> $mean #> mean #> 1.5 #> #> $sd #> sd #> 0.7071068 #> #> $se #> se #> 0.5 #> #> $mean_sd #> mean sd #> 1.5000000 0.7071068 #> #> $mean_se #> mean se #> 1.5 0.5 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> -4.853102 7.853102 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 1 2 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 0.7928932 2.2071068 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 0.2048328 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 1.5 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 1 2 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 1 #> #> $range #> min max #> 1 2 #> #> $min #> min #> 1 #> #> $max #> max #> 2 #> #> $median_range #> median min max #> 1.5 1.0 2.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 47.14045 #> #> $geom_mean #> geom_mean #> 1.414214 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 0.01729978 115.60839614 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 52.10922 #> ## Benefits in `rtables` contructions: dta_test <- data.frame( Group = rep(LETTERS[1:3], each = 2), sub_group = rep(letters[1:2], each = 3), x = 1:6 ) ## The summary obtained in with `rtables`: basic_table() %>% split_cols_by(var = \"Group\") %>% split_rows_by(var = \"sub_group\") %>% analyze(vars = \"x\", afun = s_summary) %>% build_table(df = dta_test) #> A B C #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> a #> n 2 1 0 #> sum 3 3 NA #> mean 1.5 3 NA #> sd 0.707106781186548 NA NA #> se 0.5 NA NA #> mean_sd 1.5, 0.707106781186548 3, NA NA #> mean_se 1.5, 0.5 3, NA NA #> Mean 95% CI -4.85310236808735, 7.85310236808735 NA NA #> Mean -/+ 1xSE 1, 2 NA NA #> Mean -/+ 1xSD 0.792893218813452, 2.20710678118655 NA NA #> Mean p-value (H0: mean = 0) 0.204832764699133 NA NA #> median 1.5 3 NA #> mad 0 0 NA #> Median 95% CI NA NA NA #> 25% and 75%-ile 1, 2 3, 3 NA #> iqr 1 0 NA #> range 1, 2 3, 3 NA #> min 1 3 NA #> max 2 3 NA #> Median (Min - Max) 1.5, 1, 2 3, 3, 3 NA #> cv 47.1404520791032 NA NA #> geom_mean 1.41421356237309 3 NA #> Geometric Mean 95% CI 0.0172997815631007, 115.608396135236 NA NA #> geom_cv 52.1092246837487 NA NA #> b #> n 0 1 2 #> sum NA 4 11 #> mean NA 4 5.5 #> sd NA NA 0.707106781186548 #> se NA NA 0.5 #> mean_sd NA 4, NA 5.5, 0.707106781186548 #> mean_se NA 4, NA 5.5, 0.5 #> Mean 95% CI NA NA -0.853102368087347, 11.8531023680873 #> Mean -/+ 1xSE NA NA 5, 6 #> Mean -/+ 1xSD NA NA 4.79289321881345, 6.20710678118655 #> Mean p-value (H0: mean = 0) NA NA 0.0577158767526089 #> median NA 4 5.5 #> mad NA 0 0 #> Median 95% CI NA NA NA #> 25% and 75%-ile NA 4, 4 5, 6 #> iqr NA 0 1 #> range NA 4, 4 5, 6 #> min NA 4 5 #> max NA 4 6 #> Median (Min - Max) NA 4, 4, 4 5.5, 5, 6 #> cv NA NA 12.8564869306645 #> geom_mean NA 4 5.47722557505166 #> Geometric Mean 95% CI NA NA 1.71994304449266, 17.4424380482025 #> geom_cv NA NA 12.945835316564 ## By comparison with `lapply`: X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group))) lapply(X, function(x) s_summary(x$x)) #> $A.a #> $A.a$n #> n #> 2 #> #> $A.a$sum #> sum #> 3 #> #> $A.a$mean #> mean #> 1.5 #> #> $A.a$sd #> sd #> 0.7071068 #> #> $A.a$se #> se #> 0.5 #> #> $A.a$mean_sd #> mean sd #> 1.5000000 0.7071068 #> #> $A.a$mean_se #> mean se #> 1.5 0.5 #> #> $A.a$mean_ci #> mean_ci_lwr mean_ci_upr #> -4.853102 7.853102 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $A.a$mean_sei #> mean_sei_lwr mean_sei_upr #> 1 2 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $A.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 0.7928932 2.2071068 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $A.a$mean_pval #> p_value #> 0.2048328 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $A.a$median #> median #> 1.5 #> #> $A.a$mad #> mad #> 0 #> #> $A.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $A.a$quantiles #> quantile_0.25 quantile_0.75 #> 1 2 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $A.a$iqr #> iqr #> 1 #> #> $A.a$range #> min max #> 1 2 #> #> $A.a$min #> min #> 1 #> #> $A.a$max #> max #> 2 #> #> $A.a$median_range #> median min max #> 1.5 1.0 2.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $A.a$cv #> cv #> 47.14045 #> #> $A.a$geom_mean #> geom_mean #> 1.414214 #> #> $A.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 0.01729978 115.60839614 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $A.a$geom_cv #> geom_cv #> 52.10922 #> #> #> $B.a #> $B.a$n #> n #> 1 #> #> $B.a$sum #> sum #> 3 #> #> $B.a$mean #> mean #> 3 #> #> $B.a$sd #> sd #> NA #> #> $B.a$se #> se #> NA #> #> $B.a$mean_sd #> mean sd #> 3 NA #> #> $B.a$mean_se #> mean se #> 3 NA #> #> $B.a$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $B.a$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $B.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $B.a$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $B.a$median #> median #> 3 #> #> $B.a$mad #> mad #> 0 #> #> $B.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $B.a$quantiles #> quantile_0.25 quantile_0.75 #> 3 3 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $B.a$iqr #> iqr #> 0 #> #> $B.a$range #> min max #> 3 3 #> #> $B.a$min #> min #> 3 #> #> $B.a$max #> max #> 3 #> #> $B.a$median_range #> median min max #> 3 3 3 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $B.a$cv #> cv #> NA #> #> $B.a$geom_mean #> geom_mean #> 3 #> #> $B.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $B.a$geom_cv #> geom_cv #> NA #> #> #> $C.a #> $C.a$n #> n #> 0 #> #> $C.a$sum #> sum #> NA #> #> $C.a$mean #> mean #> NA #> #> $C.a$sd #> sd #> NA #> #> $C.a$se #> se #> NA #> #> $C.a$mean_sd #> mean sd #> NA NA #> #> $C.a$mean_se #> mean se #> NA NA #> #> $C.a$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $C.a$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $C.a$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $C.a$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $C.a$median #> median #> NA #> #> $C.a$mad #> mad #> NA #> #> $C.a$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $C.a$quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $C.a$iqr #> iqr #> NA #> #> $C.a$range #> min max #> NA NA #> #> $C.a$min #> min #> NA #> #> $C.a$max #> max #> NA #> #> $C.a$median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $C.a$cv #> cv #> NA #> #> $C.a$geom_mean #> geom_mean #> NaN #> #> $C.a$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $C.a$geom_cv #> geom_cv #> NA #> #> #> $A.b #> $A.b$n #> n #> 0 #> #> $A.b$sum #> sum #> NA #> #> $A.b$mean #> mean #> NA #> #> $A.b$sd #> sd #> NA #> #> $A.b$se #> se #> NA #> #> $A.b$mean_sd #> mean sd #> NA NA #> #> $A.b$mean_se #> mean se #> NA NA #> #> $A.b$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $A.b$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $A.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $A.b$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $A.b$median #> median #> NA #> #> $A.b$mad #> mad #> NA #> #> $A.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $A.b$quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $A.b$iqr #> iqr #> NA #> #> $A.b$range #> min max #> NA NA #> #> $A.b$min #> min #> NA #> #> $A.b$max #> max #> NA #> #> $A.b$median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $A.b$cv #> cv #> NA #> #> $A.b$geom_mean #> geom_mean #> NaN #> #> $A.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $A.b$geom_cv #> geom_cv #> NA #> #> #> $B.b #> $B.b$n #> n #> 1 #> #> $B.b$sum #> sum #> 4 #> #> $B.b$mean #> mean #> 4 #> #> $B.b$sd #> sd #> NA #> #> $B.b$se #> se #> NA #> #> $B.b$mean_sd #> mean sd #> 4 NA #> #> $B.b$mean_se #> mean se #> 4 NA #> #> $B.b$mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $B.b$mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $B.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $B.b$mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $B.b$median #> median #> 4 #> #> $B.b$mad #> mad #> 0 #> #> $B.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $B.b$quantiles #> quantile_0.25 quantile_0.75 #> 4 4 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $B.b$iqr #> iqr #> 0 #> #> $B.b$range #> min max #> 4 4 #> #> $B.b$min #> min #> 4 #> #> $B.b$max #> max #> 4 #> #> $B.b$median_range #> median min max #> 4 4 4 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $B.b$cv #> cv #> NA #> #> $B.b$geom_mean #> geom_mean #> 4 #> #> $B.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $B.b$geom_cv #> geom_cv #> NA #> #> #> $C.b #> $C.b$n #> n #> 2 #> #> $C.b$sum #> sum #> 11 #> #> $C.b$mean #> mean #> 5.5 #> #> $C.b$sd #> sd #> 0.7071068 #> #> $C.b$se #> se #> 0.5 #> #> $C.b$mean_sd #> mean sd #> 5.5000000 0.7071068 #> #> $C.b$mean_se #> mean se #> 5.5 0.5 #> #> $C.b$mean_ci #> mean_ci_lwr mean_ci_upr #> -0.8531024 11.8531024 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $C.b$mean_sei #> mean_sei_lwr mean_sei_upr #> 5 6 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $C.b$mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 4.792893 6.207107 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $C.b$mean_pval #> p_value #> 0.05771588 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $C.b$median #> median #> 5.5 #> #> $C.b$mad #> mad #> 0 #> #> $C.b$median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $C.b$quantiles #> quantile_0.25 quantile_0.75 #> 5 6 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $C.b$iqr #> iqr #> 1 #> #> $C.b$range #> min max #> 5 6 #> #> $C.b$min #> min #> 5 #> #> $C.b$max #> max #> 6 #> #> $C.b$median_range #> median min max #> 5.5 5.0 6.0 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $C.b$cv #> cv #> 12.85649 #> #> $C.b$geom_mean #> geom_mean #> 5.477226 #> #> $C.b$geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 1.719943 17.442438 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $C.b$geom_cv #> geom_cv #> 12.94584 #> #> # `s_summary.factor` ## Basic usage: s_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\"))) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> # Empty factor returns zero-filled items. s_summary(factor(levels = c(\"a\", \"b\", \"c\"))) #> $n #> [1] 0 #> #> $count #> $count$a #> [1] 0 #> #> $count$b #> [1] 0 #> #> $count$c #> [1] 0 #> #> #> $count_fraction #> $count_fraction$a #> [1] 0 0 #> #> $count_fraction$b #> [1] 0 0 #> #> $count_fraction$c #> [1] 0 0 #> #> #> $fraction #> $fraction$a #> num denom #> 0 0 #> #> $fraction$b #> num denom #> 0 0 #> #> $fraction$c #> num denom #> 0 0 #> #> #> $n_blq #> [1] 0 #> ## Management of NA values. x <- factor(c(NA, \"Female\")) x <- explicit_na(x) s_summary(x, na.rm = TRUE) #> $n #> [1] 1 #> #> $count #> $count$Female #> [1] 1 #> #> #> $count_fraction #> $count_fraction$Female #> [1] 1 1 #> #> #> $fraction #> $fraction$Female #> num denom #> 1 1 #> #> #> $n_blq #> [1] 0 #> s_summary(x, na.rm = FALSE) #> $n #> [1] 2 #> #> $count #> $count$Female #> [1] 1 #> #> $count$`` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$Female #> [1] 1.0 0.5 #> #> $count_fraction$`` #> [1] 1.0 0.5 #> #> #> $fraction #> $fraction$Female #> num denom #> 1 2 #> #> $fraction$`` #> num denom #> 1 2 #> #> #> $n_blq #> [1] 0 #> ## Different denominators. x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.3 #> #> $count_fraction$b #> [1] 1.0 0.1 #> #> $count_fraction$c #> [1] 1.0 0.1 #> #> #> $fraction #> $fraction$a #> num denom #> 3 10 #> #> $fraction$b #> num denom #> 1 10 #> #> $fraction$c #> num denom #> 1 10 #> #> #> $n_blq #> [1] 0 #> s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.00 0.15 #> #> $count_fraction$b #> [1] 1.00 0.05 #> #> $count_fraction$c #> [1] 1.00 0.05 #> #> #> $fraction #> $fraction$a #> num denom #> 3 20 #> #> $fraction$b #> num denom #> 1 20 #> #> $fraction$c #> num denom #> 1 20 #> #> #> $n_blq #> [1] 0 #> # `s_summary.character` ## Basic usage: s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\"), .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> s_summary(c(\"a\", \"a\", \"b\", \"c\", \"a\", \"\"), .var = \"x\", na.rm = FALSE, verbose = FALSE) #> $n #> [1] 6 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`NA` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #> #> $count_fraction$b #> [1] 1.0000000 0.1666667 #> #> $count_fraction$c #> [1] 1.0000000 0.1666667 #> #> $count_fraction$`NA` #> [1] 1.0000000 0.1666667 #> #> #> $fraction #> $fraction$a #> num denom #> 3 6 #> #> $fraction$b #> num denom #> 1 6 #> #> $fraction$c #> num denom #> 1 6 #> #> $fraction$`NA` #> num denom #> 1 6 #> #> #> $n_blq #> [1] 0 #> # `s_summary.logical` ## Basic usage: s_summary(c(TRUE, FALSE, TRUE, TRUE)) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.75 #> #> $n_blq #> [1] 0 #> # Empty factor returns zero-filled items. s_summary(as.logical(c())) #> $n #> [1] 0 #> #> $count #> [1] 0 #> #> $count_fraction #> [1] 0 0 #> #> $n_blq #> [1] 0 #> ## Management of NA values. x <- c(NA, TRUE, FALSE) s_summary(x, na.rm = TRUE) #> $n #> [1] 2 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.5 #> #> $n_blq #> [1] 0 #> s_summary(x, na.rm = FALSE) #> $n #> [1] 3 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0000000 0.3333333 #> #> $n_blq #> [1] 0 #> ## Different denominators. x <- c(TRUE, FALSE, TRUE, TRUE) s_summary(x, denom = \"N_row\", .N_row = 10L) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.0 0.3 #> #> $n_blq #> [1] 0 #> s_summary(x, denom = \"N_col\", .N_col = 20L) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.15 #> #> $n_blq #> [1] 0 #> a_summary(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 5 0 n #> 2 a 3 0 a #> 3 b 1 0 b #> 4 c 1 0 c #> 5 a 3 (60%) 0 a #> 6 b 1 (20%) 0 b #> 7 c 1 (20%) 0 c #> 8 a 3 (60.0%) 0 a #> 9 b 1 (20.0%) 0 b #> 10 c 1 (20.0%) 0 c #> 11 a 3/5 (60.0%) 0 a #> 12 b 1/5 (20.0%) 0 b #> 13 c 1/5 (20.0%) 0 c #> 14 n_blq 0 0 n_blq a_summary( factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")), .ref_group = factor(c(\"a\", \"a\", \"b\", \"c\")), compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 5 0 #> 2 a 3 0 #> 3 b 1 0 #> 4 c 1 0 #> 5 a 3 (60%) 0 #> 6 b 1 (20%) 0 #> 7 c 1 (20%) 0 #> 8 a 3 (60.0%) 0 #> 9 b 1 (20.0%) 0 #> 10 c 1 (20.0%) 0 #> 11 a 3/5 (60.0%) 0 #> 12 b 1/5 (20.0%) 0 #> 13 c 1/5 (20.0%) 0 #> 14 n_blq 0 0 #> 15 p-value (chi-squared test) 0.9560 0 #> row_label #> 1 n #> 2 a #> 3 b #> 4 c #> 5 a #> 6 b #> 7 c #> 8 a #> 9 b #> 10 c #> 11 a #> 12 b #> 13 c #> 14 n_blq #> 15 p-value (chi-squared test) a_summary(c(\"A\", \"B\", \"A\", \"C\"), .var = \"x\", .N_col = 10, .N_row = 10, verbose = FALSE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 4 0 n #> 2 A 2 0 A #> 3 B 1 0 B #> 4 C 1 0 C #> 5 A 2 (50%) 0 A #> 6 B 1 (25%) 0 B #> 7 C 1 (25%) 0 C #> 8 A 2 (50.0%) 0 A #> 9 B 1 (25.0%) 0 B #> 10 C 1 (25.0%) 0 C #> 11 A 2/4 (50.0%) 0 A #> 12 B 1/4 (25.0%) 0 B #> 13 C 1/4 (25.0%) 0 C #> 14 n_blq 0 0 n_blq a_summary( c(\"A\", \"B\", \"A\", \"C\"), .ref_group = c(\"B\", \"A\", \"C\"), .var = \"x\", compare = TRUE, verbose = FALSE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 4 0 #> 2 A 2 0 #> 3 B 1 0 #> 4 C 1 0 #> 5 A 2 (50%) 0 #> 6 B 1 (25%) 0 #> 7 C 1 (25%) 0 #> 8 A 2 (50.0%) 0 #> 9 B 1 (25.0%) 0 #> 10 C 1 (25.0%) 0 #> 11 A 2/4 (50.0%) 0 #> 12 B 1/4 (25.0%) 0 #> 13 C 1/4 (25.0%) 0 #> 14 n_blq 0 0 #> 15 p-value (chi-squared test) 0.9074 0 #> row_label #> 1 n #> 2 A #> 3 B #> 4 C #> 5 A #> 6 B #> 7 C #> 8 A #> 9 B #> 10 C #> 11 A #> 12 B #> 13 C #> 14 n_blq #> 15 p-value (chi-squared test) a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 5 0 n #> 2 count 3 0 count #> 3 count_fraction 3 (60%) 0 count_fraction #> 4 count_fraction 3 (60.0%) 0 count_fraction #> 5 fraction 0 fraction #> 6 n_blq 0 0 n_blq a_summary( c(TRUE, FALSE, FALSE, TRUE, TRUE), .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 5 0 #> 2 count 3 0 #> 3 count_fraction 3 (60%) 0 #> 4 count_fraction 3 (60.0%) 0 #> 5 fraction 0 #> 6 n_blq 0 0 #> 7 p-value (chi-squared test) 0 #> row_label #> 1 n #> 2 count #> 3 count_fraction #> 4 count_fraction #> 5 fraction #> 6 n_blq #> 7 p-value (chi-squared test) a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = \"bla\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 10 0 #> 2 Sum -4.4 0 #> 3 Mean -0.4 0 #> 4 SD 1.1 0 #> 5 SE 0.4 0 #> 6 Mean (SD) -0.4 (1.1) 0 #> 7 Mean (SE) -0.4 (0.4) 0 #> 8 Mean 95% CI (-1.24, 0.36) 0 #> 9 Mean -/+ 1xSE (-0.79, -0.09) 0 #> 10 Mean -/+ 1xSD (-1.56, 0.68) 0 #> 11 Mean p-value (H0: mean = 0) 0.2432 0 #> 12 Median -0.2 0 #> 13 Median Absolute Deviation 0.0 0 #> 14 Median 95% CI (-1.82, 0.62) 0 #> 15 25% and 75%-ile -1.4 - 0.3 0 #> 16 IQR 1.7 0 #> 17 Min - Max -2.4 - 1.1 0 #> 18 Minimum -2.4 0 #> 19 Maximum 1.1 0 #> 20 Median (Min - Max) -0.2 (-2.4 - 1.1) 0 #> 21 CV (%) -253.2 0 #> 22 Geometric Mean NA 0 #> 23 Geometric Mean 95% CI NA 0 #> 24 CV % Geometric Mean NA 0 #> row_label #> 1 n #> 2 Sum #> 3 Mean #> 4 SD #> 5 SE #> 6 Mean (SD) #> 7 Mean (SE) #> 8 Mean 95% CI #> 9 Mean -/+ 1xSE #> 10 Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12 Median #> 13 Median Absolute Deviation #> 14 Median 95% CI #> 15 25% and 75%-ile #> 16 IQR #> 17 Min - Max #> 18 Minimum #> 19 Maximum #> 20 Median (Min - Max) #> 21 CV (%) #> 22 Geometric Mean #> 23 Geometric Mean 95% CI #> 24 CV % Geometric Mean a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = \"bla\", compare = TRUE) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 n 10 0 #> 2 Sum 48.2 0 #> 3 Mean 4.8 0 #> 4 SD 1.2 0 #> 5 SE 0.4 0 #> 6 Mean (SD) 4.8 (1.2) 0 #> 7 Mean (SE) 4.8 (0.4) 0 #> 8 Mean 95% CI (3.98, 5.66) 0 #> 9 Mean -/+ 1xSE (4.45, 5.19) 0 #> 10 Mean -/+ 1xSD (3.65, 6.00) 0 #> 11 Mean p-value (H0: mean = 0) <0.0001 0 #> 12 Median 4.7 0 #> 13 Median Absolute Deviation 0.0 0 #> 14 Median 95% CI (3.37, 5.63) 0 #> 15 25% and 75%-ile 4.1 - 5.5 0 #> 16 IQR 1.5 0 #> 17 Min - Max 3.1 - 7.1 0 #> 18 Minimum 3.1 0 #> 19 Maximum 7.1 0 #> 20 Median (Min - Max) 4.7 (3.1 - 7.1) 0 #> 21 CV (%) 24.4 0 #> 22 Geometric Mean 4.7 0 #> 23 Geometric Mean 95% CI (3.93, 5.60) 0 #> 24 CV % Geometric Mean 25.2 0 #> 25 p-value (t-test) <0.0001 0 #> row_label #> 1 n #> 2 Sum #> 3 Mean #> 4 SD #> 5 SE #> 6 Mean (SD) #> 7 Mean (SE) #> 8 Mean 95% CI #> 9 Mean -/+ 1xSE #> 10 Mean -/+ 1xSD #> 11 Mean p-value (H0: mean = 0) #> 12 Median #> 13 Median Absolute Deviation #> 14 Median 95% CI #> 15 25% and 75%-ile #> 16 IQR #> 17 Min - Max #> 18 Minimum #> 19 Maximum #> 20 Median (Min - Max) #> 21 CV (%) #> 22 Geometric Mean #> 23 Geometric Mean 95% CI #> 24 CV % Geometric Mean #> 25 p-value (t-test)"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyze numeric variables in columns — analyze_vars_in_cols","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"layout-creating function analyze_vars_in_cols() creates layout element generate column-wise analysis table. function sets analysis methods column labels wrapper rtables::analyze_colvars(). designed principally PK tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"","code":"analyze_vars_in_cols( lyt, vars, ..., .stats = c(\"n\", \"mean\", \"sd\", \"se\", \"cv\", \"geom_cv\"), .labels = c(n = \"n\", mean = \"Mean\", sd = \"SD\", se = \"SE\", cv = \"CV (%)\", geom_cv = \"CV % Geometric Mean\"), row_labels = NULL, do_summarize_row_groups = FALSE, split_col_vars = TRUE, imp_rule = NULL, avalcat_var = \"AVALCAT1\", cache = FALSE, .indent_mods = NULL, na_str = default_na_str(), nested = TRUE, .formats = NULL, .aligns = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/analyze_vars_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyze numeric variables in columns — analyze_vars_in_cols","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . ... additional arguments lower level functions. .stats (character) statistics select table. .labels (named character) labels statistics (without indent). row_labels (character) function works columns space, usually .labels character vector applies column space. can change row labels defining parameter named character vector names corresponding split values. defaults NULL contains one string, duplicate row label. do_summarize_row_groups (flag) defaults FALSE applies analysis current label rows. wrapper rtables::summarize_row_groups() can accept labelstr define row labels. behavior supported never need overload row labels. split_col_vars (flag) defaults TRUE puts analysis results onto columns. option allows add multiple instances functions, also nested fashion, without adding splits. split must happen one time single layout. imp_rule (string NULL) imputation rule setting. Defaults NULL imputation rule. Can also \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. order use imputation rule, avalcat_var argument must specified. See imputation_rule() details imputation. avalcat_var (string) imp_rule NULL, name variable indicates whether row data corresponds analysis value category \"BLQ\", \"LTR\", \"% h_pkparam_sort() lyt <- basic_table() %>% split_rows_by(var = \"STRATA1\", label_pos = \"topleft\") %>% split_rows_by( var = \"SEX\", label_pos = \"topleft\", child_labels = \"hidden\" ) %>% # Removes duplicated labels analyze_vars_in_cols(vars = \"AGE\") result <- build_table(lyt = lyt, df = adpp) result #> STRATA1 #> SEX n Mean SD SE CV (%) CV % Geometric Mean #> ———————————————————————————————————————————————————————————————— #> A #> F 81 38.8 5.4 0.6 13.8 14.3 #> M 81 38.9 5.9 0.7 15.1 14.9 #> B #> F 90 36.0 6.4 0.7 17.7 17.9 #> M 81 36.4 6.5 0.7 17.9 17.8 #> C #> F 117 34.1 6.2 0.6 18.2 18.3 #> M 72 33.2 11.5 1.4 34.6 31.6 # By selecting just some statistics and ad-hoc labels lyt <- basic_table() %>% split_rows_by(var = \"ARM\", label_pos = \"topleft\") %>% split_rows_by( var = \"SEX\", label_pos = \"topleft\", child_labels = \"hidden\", split_fun = drop_split_levels ) %>% analyze_vars_in_cols( vars = \"AGE\", .stats = c(\"n\", \"cv\", \"geom_mean\"), .labels = c( n = \"aN\", cv = \"aCV\", geom_mean = \"aGeomMean\" ) ) result <- build_table(lyt = lyt, df = adpp) result #> ARM #> SEX aN aCV aGeomMean #> ——————————————————————————————————————— #> A: Drug X #> B: Placebo #> C: Combination #> F 288 17.6 35.5 #> M 234 23.4 35.3 # Changing row labels lyt <- basic_table() %>% analyze_vars_in_cols( vars = \"AGE\", row_labels = \"some custom label\" ) result <- build_table(lyt, df = adpp) result #> n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————————— #> some custom label 522 36.1 7.4 0.3 20.4 20.6 # Pharmacokinetic parameters lyt <- basic_table() %>% split_rows_by( var = \"TLG_DISPLAY\", split_label = \"PK Parameter\", label_pos = \"topleft\", child_labels = \"hidden\" ) %>% analyze_vars_in_cols( vars = \"AVAL\" ) result <- build_table(lyt, df = adpp) result #> PK Parameter n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax 58 29.7 5.6 0.7 19.0 19.3 #> AUCinf obs 58 207.5 34.9 4.6 16.8 17.3 #> CL obs 58 5.1 1.0 0.1 20.6 22.7 #> Ae 58 1.5 0.3 0.0 21.3 24.1 #> Fe 58 15.7 3.6 0.5 22.7 24.0 #> CLR 58 0.0 0.0 0.0 19.9 22.2 #> Rmax 58 9.6 2.0 0.3 21.1 21.6 #> Tonset 58 3.0 0.7 0.1 22.4 23.3 #> RENALCLD 58 0.0 0.0 0.0 19.0 19.4 # Multiple calls (summarize label and analyze underneath) lyt <- basic_table() %>% split_rows_by( var = \"TLG_DISPLAY\", split_label = \"PK Parameter\", label_pos = \"topleft\" ) %>% analyze_vars_in_cols( vars = \"AVAL\", do_summarize_row_groups = TRUE # does a summarize level ) %>% split_rows_by(\"SEX\", child_labels = \"hidden\", label_pos = \"topleft\" ) %>% analyze_vars_in_cols( vars = \"AVAL\", split_col_vars = FALSE # avoids re-splitting the columns ) result <- build_table(lyt, df = adpp) result #> PK Parameter #> SEX n Mean SD SE CV (%) CV % Geometric Mean #> ————————————————————————————————————————————————————————————————————— #> Cmax 58 29.7 5.6 0.7 19.0 19.3 #> F 32 30.1 6.1 1.1 20.4 21.0 #> M 26 29.2 5.0 1.0 17.2 17.2 #> AUCinf obs 58 207.5 34.9 4.6 16.8 17.3 #> F 32 209.5 43.3 7.7 20.7 21.6 #> M 26 205.0 20.9 4.1 10.2 10.4 #> CL obs 58 5.1 1.0 0.1 20.6 22.7 #> F 32 5.1 1.1 0.2 20.8 23.2 #> M 26 5.1 1.1 0.2 20.7 22.5 #> Ae 58 1.5 0.3 0.0 21.3 24.1 #> F 32 1.5 0.3 0.1 19.3 20.8 #> M 26 1.5 0.4 0.1 23.9 27.9 #> Fe 58 15.7 3.6 0.5 22.7 24.0 #> F 32 16.3 3.4 0.6 20.9 22.3 #> M 26 15.0 3.7 0.7 24.7 25.6 #> CLR 58 0.0 0.0 0.0 19.9 22.2 #> F 32 0.0 0.0 0.0 20.5 24.8 #> M 26 0.0 0.0 0.0 19.5 18.9 #> Rmax 58 9.6 2.0 0.3 21.1 21.6 #> F 32 9.6 2.2 0.4 22.4 23.5 #> M 26 9.6 1.9 0.4 19.7 19.5 #> Tonset 58 3.0 0.7 0.1 22.4 23.3 #> F 32 3.0 0.7 0.1 24.7 25.9 #> M 26 3.0 0.6 0.1 19.8 20.0 #> RENALCLD 58 0.0 0.0 0.0 19.0 19.4 #> F 32 0.0 0.0 0.0 19.6 20.4 #> M 26 0.0 0.0 0.0 18.0 17.6"},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add variable labels to top left corner in table — append_varlabels","title":"Add variable labels to top left corner in table — append_varlabels","text":"Helper layout-creating function append variable labels given variables vector given dataset top left corner. variable label found variable name used instead. Multiple variable labels concatenated slashes.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add variable labels to top left corner in table — append_varlabels","text":"","code":"append_varlabels(lyt, df, vars, indent = 0L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add variable labels to top left corner in table — append_varlabels","text":"lyt (PreDataTableLayouts) layout analyses added . df (data.frame) data set containing analysis variables. vars (character) variable names labels looked df. indent (integer(1)) non-negative number nested indent space, default 0L means indent. 1L means two spaces indent, 2L means four spaces indent .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add variable labels to top left corner in table — append_varlabels","text":"modified layout new variable label(s) added top-left material.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add variable labels to top left corner in table — append_varlabels","text":"optimal implementation course, since using data set layout creation. mature rtables implementation also improved necessary anymore.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/append_varlabels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add variable labels to top left corner in table — append_varlabels","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"SEX\") %>% append_varlabels(DM, \"SEX\") %>% analyze(\"AGE\", afun = mean) %>% append_varlabels(DM, \"AGE\", indent = 1) build_table(lyt, DM) #> SEX A: Drug X B: Placebo C: Combination #> Age (N=121) (N=106) (N=129) #> ————————————————————————————————————————————————————————————————————————— #> F #> mean 33.7142857142857 33.8392857142857 34.8852459016393 #> M #> mean 36.5490196078431 32.1 34.2794117647059 #> U #> mean NA NA NA #> UNDIFFERENTIATED #> mean NA NA NA lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"AGE\", afun = mean) %>% append_varlabels(DM, c(\"SEX\", \"AGE\")) build_table(lyt, DM) #> SEX / Age A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————————— #> F #> mean 33.7142857142857 33.8392857142857 34.8852459016393 #> M #> mean 36.5490196078431 32.1 34.2794117647059 #> U #> mean NA NA NA #> UNDIFFERENTIATED #> mean NA NA NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply automatic formatting — apply_auto_formatting","title":"Apply automatic formatting — apply_auto_formatting","text":"Checks listed formats .formats \"auto\", replaces \"auto\" correct implementation format_auto given statistics, data, variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply automatic formatting — apply_auto_formatting","text":"","code":"apply_auto_formatting(.formats, x_stats, .df_row, .var)"},{"path":"https://insightsengineering.github.io/tern/main/reference/apply_auto_formatting.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply automatic formatting — apply_auto_formatting","text":".formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. x_stats (named list) named list statistics element corresponds element .formats, matching names. .df_row (data.frame) data frame across columns given row split. .var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":null,"dir":"Reference","previous_headings":"","what":"Standard arguments — argument_convention","title":"Standard arguments — argument_convention","text":"documentation function lists arguments tern used repeatedly express analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standard arguments — argument_convention","text":"... additional arguments lower level functions. .aligns (character NULL) alignment table contents (including labels). NULL, \"center\" applied. See formatters::list_valid_aligns() list currently supported alignments. .all_col_counts (integer) vector value represents global count column. Values taken alt_counts_df specified (see rtables::build_table()). .df_row (data.frame) data frame across columns given row split. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. .ref_group (data.frame vector) data corresponding reference group. .spl_context (data.frame) gives information ancestor split states passed rtables. .stats (character) statistics select table. .var (string) single variable name passed rtables requested statistics function. add_total_level (flag) adds \"total\" level others includes levels constitute split. custom label can set level via custom_label argument. col_by (factor) defining column groups. conf_level (proportion) confidence level interval. data (data.frame) dataset containing variables summarize. df (data.frame) data set containing analysis variables. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. id (string) subject variable name. is_event (flag)TRUE event, FALSE time event censored. label_all (string) label total population analysis. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. lyt (PreDataTableLayouts) layout analyses added . method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed. na.rm (flag) whether NA values removed x prior analysis. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. prune_zero_rows (flag) whether prune zero rows. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. rsp (logical) vector indicating whether subject responder . show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. tte (numeric) vector time--event duration values. var_labels (character) variable labels. variables (named list string) list additional analysis variables. vars (character) variable names primary analysis variable iterated . var (string) single variable name primary analysis variable. x (numeric) vector numbers want analyze. xlim (numeric(2)) vector containing lower upper limits x-axis, respectively. NULL (default), default scale range used. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/argument_convention.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standard arguments — argument_convention","text":"Although function just returns NULL two uses, tern users provides documentation arguments commonly consistently used framework. developer adds single reference point import roxygen argument description : @inheritParams argument_convention","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Arrange multiple grobs — arrange_grobs","title":"Arrange multiple grobs — arrange_grobs","text":"Arrange grobs new grob n * m (rows * cols) layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Arrange multiple grobs — arrange_grobs","text":"","code":"arrange_grobs( ..., grobs = list(...), ncol = NULL, nrow = NULL, padding_ht = grid::unit(2, \"line\"), padding_wt = grid::unit(2, \"line\"), vp = NULL, gp = NULL, name = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Arrange multiple grobs — arrange_grobs","text":"... grobs. grobs (list grob) list grobs. ncol (integer(1)) number columns layout. nrow (integer(1)) number rows layout. padding_ht (grid::unit) unit length 1, vertical space grob. padding_wt (grid::unit) unit length 1, horizontal space grob. vp (viewport NULL) viewport() object (NULL). gp (gpar) gpar() object. name (string) character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Arrange multiple grobs — arrange_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/arrange_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Arrange multiple grobs — arrange_grobs","text":"","code":"library(grid) # \\donttest{ num <- lapply(1:9, textGrob) grid::grid.newpage() grid.draw(arrange_grobs(grobs = num, ncol = 2)) #> Warning: `arrange_grobs()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. showViewport() g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, nrow = 2)) showViewport() grid::grid.newpage() grid.draw(arrange_grobs(g1, g2, g3, ncol = 3)) grid::grid.newpage() grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2))) vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1)) showViewport() # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to rtable — as.rtable","title":"Convert to rtable — as.rtable","text":"new generic function convert objects rtable tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to rtable — as.rtable","text":"","code":"as.rtable(x, ...) # S3 method for class 'data.frame' as.rtable(x, format = \"xx.xx\", ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to rtable — as.rtable","text":"x (data.frame) object converted rtable. ... additional arguments methods. format (string function) format used columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to rtable — as.rtable","text":"rtables table object. Note concrete class depend method used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"methods-by-class-","dir":"Reference","previous_headings":"","what":"Methods (by class)","title":"Convert to rtable — as.rtable","text":".rtable(data.frame): Method converting data.frame contains numeric columns rtable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as.rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to rtable — as.rtable","text":"","code":"x <- data.frame( a = 1:10, b = rnorm(10) ) as.rtable(x) #> a b #> —————————————————— #> 1 1.00 0.07 #> 2 2.00 -0.64 #> 3 3.00 -0.05 #> 4 4.00 -0.25 #> 5 5.00 0.44 #> 6 6.00 2.76 #> 7 7.00 0.05 #> 8 8.00 0.58 #> 9 9.00 0.12 #> 10 10.00 -1.91"},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of a vector to a factor — as_factor_keep_attributes","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"Converts x factor keeps attributes. Warns appropriately user can decide whether prefer converting factor manually (e.g. full control factor levels).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"","code":"as_factor_keep_attributes( x, x_name = deparse(substitute(x)), na_level = \"\", verbose = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"x (vector) object convert. x_name (string) name x. na_level (string) explicit missing level used converting character vector. verbose (flag) defaults TRUE. prints warnings messages.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/as_factor_keep_attributes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of a vector to a factor — as_factor_keep_attributes","text":"factor attributes (except class) x. modify x already factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional assertions to use with checkmate — assertions","title":"Additional assertions to use with checkmate — assertions","text":"Additional assertion functions can used together checkmate package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Additional assertions to use with checkmate — assertions","text":"","code":"assert_list_of_variables(x, .var.name = checkmate::vname(x), add = NULL) assert_df_with_variables( df, variables, na_level = NULL, .var.name = checkmate::vname(df), add = NULL ) assert_valid_factor( x, min.levels = 1, max.levels = NULL, null.ok = TRUE, any.missing = TRUE, n.levels = NULL, len = NULL, .var.name = checkmate::vname(x), add = NULL ) assert_df_with_factors( df, variables, min.levels = 1, max.levels = NULL, any.missing = TRUE, na_level = NULL, .var.name = checkmate::vname(df), add = NULL ) assert_proportion_value(x, include_boundaries = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Additional assertions to use with checkmate — assertions","text":"x () object test. .var.name [character(1)] Name checked object print assertions. Defaults heuristic implemented vname. add [AssertCollection] Collection store assertion messages. See AssertCollection. df (data.frame) data set test. variables (named list character) list variables test. na_level (string) string using represent NA missing data. NA values please consider using directly .na() similar approaches. min.levels [integer(1)] Minimum number factor levels. Default NULL (check). max.levels [integer(1)] Maximum number factor levels. Default NULL (check). null.ok [logical(1)] set TRUE, x may also NULL. case type check x performed, additional checks disabled. .missing [logical(1)] vectors missing values allowed? Default TRUE. n.levels [integer(1)] Exact number factor levels. Default NULL (check). len [integer(1)] Exact expected length x. include_boundaries (flag) whether include boundaries testing proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Additional assertions to use with checkmate — assertions","text":"Nothing assertion passes, otherwise prints error message.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/assertions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Additional assertions to use with checkmate — assertions","text":"assert_list_of_variables(): Checks whether x valid list variable names. NULL elements list x dropped Filter(Negate(.null), x). assert_df_with_variables(): Check whether df data frame analysis variables. Please notice produces error variables present data.frame opposite required. assert_valid_factor(): Check whether x valid factor (.e. levels empty string levels). Note NULL NA elements allowed. assert_df_with_factors(): Check whether df data frame analysis variables factors. Note creation NA direct call factor() trim NA levels vector list . assert_proportion_value(): Check whether x proportion: number 0 1.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for bins in percent — bins_percent_labels","title":"Labels for bins in percent — bins_percent_labels","text":"creates labels quantile based bins percent. assumes right-closed intervals produced cut_quantile_bins().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for bins in percent — bins_percent_labels","text":"","code":"bins_percent_labels(probs, digits = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for bins in percent — bins_percent_labels","text":"probs (numeric) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. digits (integer(1)) number decimal places round percent numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bins_percent_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for bins in percent — bins_percent_labels","text":"character vector labels format [0%,20%], (20%,50%], etc.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":null,"dir":"Reference","previous_headings":"","what":"Bland-Altman analysis — bland_altman","title":"Bland-Altman analysis — bland_altman","text":"Functions use Bland-Altman method assess agreement two numerical vectors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bland-Altman analysis — bland_altman","text":"","code":"s_bland_altman(x, y, conf_level = 0.95) g_bland_altman(x, y, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bland-Altman analysis — bland_altman","text":"x (numeric) vector numbers want analyze. y (numeric) vector numbers want analyze, compared x. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bland-Altman analysis — bland_altman","text":"s_bland_altman() returns named list following elements: df, difference_mean, ci_mean, difference_sd, difference_se, upper_agreement_limit, lower_agreement_limit, agreement_limit_se, upper_agreement_limit_ci, lower_agreement_limit_ci, t_value, n. g_bland_altman() returns ggplot Bland-Altman plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Bland-Altman analysis — bland_altman","text":"s_bland_altman(): Statistics function compares two numeric vectors using Bland-Altman method calculates variety statistics. g_bland_altman(): Graphing function produces Bland-Altman plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/bland_altman.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bland-Altman analysis — bland_altman","text":"","code":"x <- seq(1, 60, 5) y <- seq(5, 50, 4) conf_level <- 0.9 # Derive statistics that are needed for Bland-Altman plot s_bland_altman(x, y, conf_level = conf_level) #> $df #> average difference #> 1 3.0 -4 #> 2 7.5 -3 #> 3 12.0 -2 #> 4 16.5 -1 #> 5 21.0 0 #> 6 25.5 1 #> 7 30.0 2 #> 8 34.5 3 #> 9 39.0 4 #> 10 43.5 5 #> 11 48.0 6 #> 12 52.5 7 #> #> $difference_mean #> [1] 1.5 #> #> $ci_mean #> [1] -0.3692162 3.3692162 #> #> $difference_sd #> [1] 3.605551 #> #> $difference_se #> [1] 1.040833 #> #> $upper_agreement_limit #> [1] 7.430604 #> #> $lower_agreement_limit #> [1] -4.430604 #> #> $agreement_limit_se #> [1] 1.802776 #> #> $upper_agreement_limit_ci #> [1] 4.193027 10.668181 #> #> $lower_agreement_limit_ci #> [1] -7.668181 -1.193027 #> #> $t_value #> [1] 1.795885 #> #> $n #> [1] 12 #> # Create a Bland-Altman plot g_bland_altman(x = x, y = y, conf_level = conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Content row function to add row total to labels — c_label_n","title":"Content row function to add row total to labels — c_label_n","text":"takes label latest row split level adds row total df parentheses. function differs c_label_n_alt() taking row counts df rather alt_counts_df, used add_rowcounts() alt_counts set FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content row function to add row total to labels — c_label_n","text":"","code":"c_label_n(df, labelstr, .N_row)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content row function to add row total to labels — c_label_n","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content row function to add row total to labels — c_label_n","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Content row function to add row total to labels — c_label_n","text":"important use df rather .N_row implementation, former already split columns refer first column data .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":null,"dir":"Reference","previous_headings":"","what":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"takes label latest row split level adds row total alt_counts_df parentheses. function differs c_label_n() taking row counts alt_counts_df rather df, used add_rowcounts() alt_counts set TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"","code":"c_label_n_alt(df, labelstr, .alt_df_row)"},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/c_label_n_alt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Content row function to add alt_counts_df row total to labels — c_label_n_alt","text":"list formatted rtables::CellValue() row count value correct label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for content functions given a data frame with flag input — cfun_by_flag","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"can useful tabulating model results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"","code":"cfun_by_flag(analysis_var, flag_var, format = \"xx\", .indent_mods = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"analysis_var (string) variable name column containing values returned content function. flag_var (string) variable name logical column identifying row returned. format (string)rtables format use.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cfun_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for content functions given a data frame with flag input — cfun_by_flag","text":"content function gives df$analysis_var row identified .df_row$flag given format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Check proportion difference arguments — check_diff_prop_ci","title":"Check proportion difference arguments — check_diff_prop_ci","text":"Verifies /convert arguments valid values used estimation difference responder proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check proportion difference arguments — check_diff_prop_ci","text":"","code":"check_diff_prop_ci(rsp, grp, strata = NULL, conf_level, correct = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/check_diff_prop_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check proportion difference arguments — check_diff_prop_ci","text":"rsp (logical) vector indicating whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). strata (factor) variable one level per stratum length rsp. conf_level (proportion) confidence level interval. correct (flag) whether include continuity correction. information, see stats::prop.test().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":null,"dir":"Reference","previous_headings":"","what":"Check element dimension — check_same_n","title":"Check element dimension — check_same_n","text":"Checks elements ... dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check element dimension — check_same_n","text":"","code":"check_same_n(..., omit_null = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check element dimension — check_same_n","text":"... (data.frame vector) data frames vectors. omit_null (flag) whether NULL elements ... omitted check.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/check_same_n.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check element dimension — check_same_n","text":"logical value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Class for CombinationFunction — combination_function","title":"Class for CombinationFunction — combination_function","text":"CombinationFunction S4 class extends standard functions. special functions can combined negated logical operators.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Class for CombinationFunction — combination_function","text":"","code":"# S4 method for class 'CombinationFunction,CombinationFunction' e1 & e2 # S4 method for class 'CombinationFunction,CombinationFunction' e1 | e2 # S4 method for class 'CombinationFunction' !x"},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Class for CombinationFunction — combination_function","text":"e1 (CombinationFunction) left hand side logical operator. e2 (CombinationFunction) right hand side logical operator. x (CombinationFunction) function negated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Class for CombinationFunction — combination_function","text":"logical value indicating whether left hand side equation equals right hand side.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Class for CombinationFunction — combination_function","text":"e1 & e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. e1 | e2: Logical \"\" combination CombinationFunction functions. resulting object class, evaluates two argument functions. result \"\" two individual results. `!`(CombinationFunction): Logical negation CombinationFunction functions. resulting object class, evaluates original function. result opposite results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combination_function.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Class for CombinationFunction — combination_function","text":"","code":"higher <- function(a) { force(a) CombinationFunction( function(x) { x > a } ) } lower <- function(b) { force(b) CombinationFunction( function(x) { x < b } ) } c1 <- higher(5) c2 <- lower(10) c3 <- higher(5) & lower(10) c3(7) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine counts — combine_counts","title":"Combine counts — combine_counts","text":"Simplifies estimation column counts, especially group combination required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine counts — combine_counts","text":"","code":"combine_counts(fct, groups_list = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine counts — combine_counts","text":"fct (factor) variable levels needs grouped. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine counts — combine_counts","text":"vector column counts.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine counts — combine_counts","text":"","code":"ref <- c(\"A: Drug X\", \"B: Placebo\") groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts( fct = DM$ARM, groups_list = groups ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM, col_counts = col_counts) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ————————————————————————————————————————————————— #> n 227 129 #> Mean (SD) 34.0 (7.2) 34.6 (6.5) #> Median 33.0 33.0 #> Min - Max 20.0 - 60.0 22.0 - 53.0 ref <- \"A: Drug X\" groups <- combine_groups(fct = DM$ARM, ref = ref) col_counts <- combine_counts( fct = DM$ARM, groups_list = groups ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM, col_counts = col_counts) #> A: Drug X B: Placebo/C: Combination #> (N=121) (N=235) #> ——————————————————————————————————————————————————— #> n 121 235 #> Mean (SD) 34.9 (7.8) 33.9 (6.5) #> Median 33.0 33.0 #> Min - Max 20.0 - 60.0 21.0 - 55.0"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Reference and treatment group combination — combine_groups","title":"Reference and treatment group combination — combine_groups","text":"Facilitate re-combination groups divided reference treatment groups; helps arranging groups columns rtables framework teal modules.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reference and treatment group combination — combine_groups","text":"","code":"combine_groups(fct, ref = NULL, collapse = \"/\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reference and treatment group combination — combine_groups","text":"fct (factor) variable levels needs grouped. ref (character) reference level(s). collapse (string) character string separate fct ref.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reference and treatment group combination — combine_groups","text":"list first item ref (reference) second item trt (treatment).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reference and treatment group combination — combine_groups","text":"","code":"groups <- combine_groups( fct = DM$ARM, ref = c(\"B: Placebo\") ) basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze_vars(\"AGE\") %>% build_table(DM) #> B: Placebo A: Drug X/C: Combination #> (N=106) (N=250) #> —————————————————————————————————————————————————— #> n 106 250 #> Mean (SD) 33.0 (6.3) 34.7 (7.1) #> Median 32.0 33.0 #> Min - Max 21.0 - 55.0 20.0 - 60.0"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine factor levels — combine_levels","title":"Combine factor levels — combine_levels","text":"Combine specified old factor Levels single new level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine factor levels — combine_levels","text":"","code":"combine_levels(x, levels, new_level = paste(levels, collapse = \"/\"))"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine factor levels — combine_levels","text":"x (factor) factor variable. levels (character) level names combined. new_level (string) name new level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine factor levels — combine_levels","text":"factor new levels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine factor levels — combine_levels","text":"","code":"x <- factor(letters[1:5], levels = letters[5:1]) combine_levels(x, levels = c(\"a\", \"b\")) #> [1] a/b a/b c d e #> Levels: e d c a/b combine_levels(x, c(\"e\", \"b\")) #> [1] a e/b c d e/b #> Levels: e/b d c a"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Element-wise combination of two vectors — combine_vectors","title":"Element-wise combination of two vectors — combine_vectors","text":"Element-wise combination two vectors","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Element-wise combination of two vectors — combine_vectors","text":"","code":"combine_vectors(x, y)"},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Element-wise combination of two vectors — combine_vectors","text":"x (vector) first vector combine. y (vector) second vector combine.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Element-wise combination of two vectors — combine_vectors","text":"list element combines corresponding elements x y.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/combine_vectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Element-wise combination of two vectors — combine_vectors","text":"","code":"combine_vectors(1:3, 4:6) #> [[1]] #> [1] 1 4 #> #> [[2]] #> [1] 2 5 #> #> [[3]] #> [1] 3 6 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare variables between groups — compare_variables","title":"Compare variables between groups — compare_variables","text":"analyze function compare_vars() creates layout element summarize compare one variables, using S3 generic function s_summary() calculate list summary statistics. list available statistics numeric variables can viewed running get_stats(\"analyze_vars_numeric\", add_pval = TRUE) non-numeric variables running get_stats(\"analyze_vars_counts\", add_pval = TRUE). Use .stats parameter specify statistics include output summary table. Prior using function table layout must use rtables::split_cols_by() create column split variable used comparisons, specify reference group via ref_group parameter. Comparisons can performed group (column) specified reference group including p-value statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare variables between groups — compare_variables","text":"","code":"compare_vars( lyt, vars, var_labels = vars, na_str = default_na_str(), nested = TRUE, ..., na.rm = TRUE, show_labels = \"default\", table_names = vars, section_div = NA_character_, .stats = c(\"n\", \"mean_sd\", \"count_fraction\", \"pval\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_compare(x, .ref_group, .in_ref_col, ...) # S3 method for class 'numeric' s_compare(x, .ref_group, .in_ref_col, ...) # S3 method for class 'factor' s_compare(x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, ...) # S3 method for class 'character' s_compare( x, .ref_group, .in_ref_col, denom = \"n\", na.rm = TRUE, .var, verbose = TRUE, ... ) # S3 method for class 'logical' s_compare(x, .ref_group, .in_ref_col, na.rm = TRUE, denom = \"n\", ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare variables between groups — compare_variables","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_compare(). na.rm (flag) whether NA values removed x prior analysis. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. section_div (string) string repeated section divider group defined split instruction, NA_character_ (default) section divider. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric\", add_pval = TRUE) see statistics available numeric variables, get_stats(\"analyze_vars_counts\", add_pval = TRUE) statistics available non-numeric variables. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label. x (numeric) vector numbers want analyze. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. denom (string) choice denominator factor proportions, can n (number values row column intersection). .var (string) single variable name passed rtables requested statistics function. verbose (flag) whether warnings messages printed. Mainly used print information factor casting. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare variables between groups — compare_variables","text":"compare_vars() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_compare() table layout. s_compare() returns output s_summary() comparisons versus reference group form p-values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Compare variables between groups — compare_variables","text":"compare_vars(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_compare(): S3 generic function produce comparison summary. s_compare(numeric): Method numeric class. uses standard t-test calculate p-value. s_compare(factor): Method factor class. uses chi-squared test calculate p-value. s_compare(character): Method character class. makes automatic conversion factor (warning) forwards method factors. s_compare(logical): Method logical class. chi-squared test used. missing values removed, counted FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare variables between groups — compare_variables","text":"factor variables, denom factor proportions can n since purpose compare proportions columns, therefore row-based proportion make sense. Proportion based N_col difficult since use counts chi-squared test statistic, therefore missing values accounted explicit factor levels. factor variables contain NA, NA values excluded default. include NA values set na.rm = FALSE missing values displayed NA level. Alternatively, explicit factor level can defined NA values pre-processing via df_explicit_na() - default na_level (\"\") also excluded na.rm set TRUE. character variables, automatic conversion factor guarantee table generated correctly. particular sparse tables likely can fail. Therefore always better manually convert character variables factors pre-processing. compare_vars(), column split must define reference group via ref_group comparison well defined.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/compare_variables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare variables between groups — compare_variables","text":"","code":"# `compare_vars()` in `rtables` pipelines ## Default output within a `rtables` pipeline. lyt <- basic_table() %>% split_cols_by(\"ARMCD\", ref_group = \"ARM B\") %>% compare_vars(c(\"AGE\", \"SEX\")) build_table(lyt, tern_ex_adsl) #> ARM A ARM B ARM C #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 69 73 58 #> Mean (SD) 34.1 (6.8) 35.8 (7.1) 36.1 (7.4) #> p-value (t-test) 0.1446 0.8212 #> SEX #> n 69 73 58 #> F 38 (55.1%) 40 (54.8%) 32 (55.2%) #> M 31 (44.9%) 33 (45.2%) 26 (44.8%) #> p-value (chi-squared test) 1.0000 1.0000 ## Select and format statistics output. lyt <- basic_table() %>% split_cols_by(\"ARMCD\", ref_group = \"ARM C\") %>% compare_vars( vars = \"AGE\", .stats = c(\"mean_sd\", \"pval\"), .formats = c(mean_sd = \"xx.x, xx.x\"), .labels = c(mean_sd = \"Mean, SD\") ) build_table(lyt, df = tern_ex_adsl) #> ARM A ARM B ARM C #> ———————————————————————————————————————————————————— #> Mean, SD 34.1, 6.8 35.8, 7.1 36.1, 7.4 #> p-value (t-test) 0.1176 0.8212 # `s_compare.numeric` ## Usual case where both this and the reference group vector have more than 1 value. s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE) #> $n #> n #> 10 #> #> $sum #> sum #> 51.27191 #> #> $mean #> mean #> 5.127191 #> #> $sd #> sd #> 1.226119 #> #> $se #> se #> 0.387733 #> #> $mean_sd #> mean sd #> 5.127191 1.226119 #> #> $mean_se #> mean se #> 5.127191 0.387733 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> 4.250078 6.004304 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 4.739458 5.514924 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 3.901071 6.353310 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 3.353908e-07 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 5.024369 #> #> $mad #> mad #> -4.440892e-16 #> #> $median_ci #> median_ci_lwr median_ci_upr #> 4.638779 5.862086 #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 4.756763 5.549828 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 0.7930643 #> #> $range #> min max #> 2.725885 7.682557 #> #> $min #> min #> 2.725885 #> #> $max #> max #> 7.682557 #> #> $median_range #> median min max #> 5.024369 2.725885 7.682557 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 23.91406 #> #> $geom_mean #> geom_mean #> 4.985435 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 4.144463 5.997052 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 26.26258 #> #> $pval #> [1] 2.25779e-08 #> ## If one group has not more than 1 value, then p-value is not calculated. s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE) #> $n #> n #> 10 #> #> $sum #> sum #> 50.71578 #> #> $mean #> mean #> 5.071578 #> #> $sd #> sd #> 1.105832 #> #> $se #> se #> 0.3496948 #> #> $mean_sd #> mean sd #> 5.071578 1.105832 #> #> $mean_se #> mean se #> 5.0715780 0.3496948 #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> 4.280513 5.862643 #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> 4.721883 5.421273 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> 3.965746 6.177410 #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> 1.511204e-07 #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> 5.260423 #> #> $mad #> mad #> 0 #> #> $median_ci #> median_ci_lwr median_ci_upr #> 3.529264 6.318293 #> attr(,\"conf_level\") #> [1] 0.9785156 #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> 4.024149 6.065057 #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> 2.040908 #> #> $range #> min max #> 3.300549 6.337320 #> #> $min #> min #> 3.300549 #> #> $max #> max #> 6.33732 #> #> $median_range #> median min max #> 5.260423 3.300549 6.337320 #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> 21.8045 #> #> $geom_mean #> geom_mean #> 4.952266 #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> 4.181833 5.864639 #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> 23.97201 #> #> $pval #> character(0) #> ## Empty numeric does not fail, it returns NA-filled items and no p-value. s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE) #> $n #> n #> 0 #> #> $sum #> sum #> NA #> #> $mean #> mean #> NA #> #> $sd #> sd #> NA #> #> $se #> se #> NA #> #> $mean_sd #> mean sd #> NA NA #> #> $mean_se #> mean se #> NA NA #> #> $mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean 95% CI\" #> #> $mean_sei #> mean_sei_lwr mean_sei_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSE\" #> #> $mean_sdi #> mean_sdi_lwr mean_sdi_upr #> NA NA #> attr(,\"label\") #> [1] \"Mean -/+ 1xSD\" #> #> $mean_pval #> p_value #> NA #> attr(,\"label\") #> [1] \"Mean p-value (H0: mean = 0)\" #> #> $median #> median #> NA #> #> $mad #> mad #> NA #> #> $median_ci #> median_ci_lwr median_ci_upr #> NA NA #> attr(,\"conf_level\") #> [1] NA #> attr(,\"label\") #> [1] \"Median 95% CI\" #> #> $quantiles #> quantile_0.25 quantile_0.75 #> NA NA #> attr(,\"label\") #> [1] \"25% and 75%-ile\" #> #> $iqr #> iqr #> NA #> #> $range #> min max #> NA NA #> #> $min #> min #> NA #> #> $max #> max #> NA #> #> $median_range #> median min max #> NA NA NA #> attr(,\"label\") #> [1] \"Median (Min - Max)\" #> #> $cv #> cv #> NA #> #> $geom_mean #> geom_mean #> NaN #> #> $geom_mean_ci #> mean_ci_lwr mean_ci_upr #> NA NA #> attr(,\"label\") #> [1] \"Geometric Mean 95% CI\" #> #> $geom_cv #> geom_cv #> NA #> #> $pval #> character(0) #> # `s_compare.factor` ## Basic usage: x <- factor(c(\"a\", \"a\", \"b\", \"c\", \"a\")) y <- factor(c(\"a\", \"b\", \"c\")) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> ## Management of NA values. x <- explicit_na(factor(c(\"a\", \"a\", \"b\", \"c\", \"a\", NA, NA))) y <- explicit_na(factor(c(\"a\", \"b\", \"c\", NA))) s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 7 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`` #> [1] 2 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0000000 0.4285714 #> #> $count_fraction$b #> [1] 1.0000000 0.1428571 #> #> $count_fraction$c #> [1] 1.0000000 0.1428571 #> #> $count_fraction$`` #> [1] 2.0000000 0.2857143 #> #> #> $fraction #> $fraction$a #> num denom #> 3 7 #> #> $fraction$b #> num denom #> 1 7 #> #> $fraction$c #> num denom #> 1 7 #> #> $fraction$`` #> num denom #> 2 7 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.9063036 #> # `s_compare.character` ## Basic usage: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\") y <- c(\"a\", \"b\", \"c\") s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> ## Note that missing values handling can make a large difference: x <- c(\"a\", \"a\", \"b\", \"c\", \"a\", NA) y <- c(\"a\", \"b\", \"c\", rep(NA, 20)) s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", verbose = FALSE ) #> $n #> [1] 5 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.6 #> #> $count_fraction$b #> [1] 1.0 0.2 #> #> $count_fraction$c #> [1] 1.0 0.2 #> #> #> $fraction #> $fraction$a #> num denom #> 3 5 #> #> $fraction$b #> num denom #> 1 5 #> #> $fraction$c #> num denom #> 1 5 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.7659283 #> s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = \"x\", na.rm = FALSE, verbose = FALSE ) #> $n #> [1] 6 #> #> $count #> $count$a #> [1] 3 #> #> $count$b #> [1] 1 #> #> $count$c #> [1] 1 #> #> $count$`` #> [1] 1 #> #> #> $count_fraction #> $count_fraction$a #> [1] 3.0 0.5 #> #> $count_fraction$b #> [1] 1.0000000 0.1666667 #> #> $count_fraction$c #> [1] 1.0000000 0.1666667 #> #> $count_fraction$`` #> [1] 1.0000000 0.1666667 #> #> #> $fraction #> $fraction$a #> num denom #> 3 6 #> #> $fraction$b #> num denom #> 1 6 #> #> $fraction$c #> num denom #> 1 6 #> #> $fraction$`` #> num denom #> 1 6 #> #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.005768471 #> # `s_compare.logical` ## Basic usage: x <- c(TRUE, FALSE, TRUE, TRUE) y <- c(FALSE, FALSE, TRUE) s_compare(x, .ref_group = y, .in_ref_col = FALSE) #> $n #> [1] 4 #> #> $count #> [1] 3 #> #> $count_fraction #> [1] 3.00 0.75 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.2702894 #> ## Management of NA values. x <- c(NA, TRUE, FALSE) y <- c(NA, NA, NA, NA, FALSE) s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE) #> $n #> [1] 2 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.5 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.3864762 #> s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE) #> $n #> [1] 3 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0000000 0.3333333 #> #> $n_blq #> [1] 0 #> #> $pval_counts #> [1] 0.1675463 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for descriptive statistics — control_analyze_vars","title":"Control function for descriptive statistics — control_analyze_vars","text":"Sets list parameters summaries descriptive statistics. Typically used internally specify details s_summary(). function family mainly used analyze_vars().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for descriptive statistics — control_analyze_vars","text":"","code":"control_analyze_vars( conf_level = 0.95, quantiles = c(0.25, 0.75), quantile_type = 2, test_mean = 0 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for descriptive statistics — control_analyze_vars","text":"conf_level (proportion) confidence level interval. quantiles (numeric(2)) vector length two specify quantiles calculate. quantile_type (numeric(1)) number 1 9 selecting quantile algorithms used. Default set 2 matches default quantile algorithm SAS proc univariate set QNTLDEF=5. differs R's default. See type stats::quantile(). test_mean (numeric(1)) number test mean null hypothesis calculating p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_analyze_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for descriptive statistics — control_analyze_vars","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Control functions for Kaplan-Meier plot annotation tables — control_annot","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"Auxiliary functions controlling arguments formatting annotation tables can added plots generated via g_km().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"","code":"control_surv_med_annot(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE) control_coxph_annot( x = 0.29, y = 0.51, w = 0.4, h = 0.125, fill = TRUE, ref_lbls = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"x (proportion) x-coordinate center annotation table. y (proportion) y-coordinate center annotation table. w (proportion) relative width annotation table. h (proportion) relative height annotation table. fill (flag character) whether annotation table background fill color. Can also color code use background fill color. TRUE, color code defaults \"#00000020\". ref_lbls (flag) whether reference group explicitly printed labels annotation table. FALSE (default), comparison groups printed table labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"control_surv_med_annot(): Control function formatting median survival time annotation table. annotation table can added g_km() setting annot_surv_med=TRUE, can configured using control_surv_med_annot() function setting control_annot_surv_med argument. control_coxph_annot(): Control function formatting Cox-PH annotation table. annotation table can added g_km() setting annot_coxph=TRUE, can configured using control_coxph_annot() function setting control_annot_coxph argument.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control functions for Kaplan-Meier plot annotation tables — control_annot","text":"","code":"control_surv_med_annot() #> $x #> [1] 0.8 #> #> $y #> [1] 0.85 #> #> $w #> [1] 0.32 #> #> $h #> [1] 0.16 #> #> $fill #> [1] TRUE #> control_coxph_annot() #> $x #> [1] 0.29 #> #> $y #> [1] 0.51 #> #> $w #> [1] 0.4 #> #> $h #> [1] 0.125 #> #> $fill #> [1] TRUE #> #> $ref_lbls #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for Cox-PH model — control_coxph","title":"Control function for Cox-PH model — control_coxph","text":"auxiliary function controlling arguments Cox-PH model, typically used internally specify details Cox-PH model s_coxph_pairwise(). conf_level refers Hazard Ratio estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for Cox-PH model — control_coxph","text":"","code":"control_coxph( pval_method = c(\"log-rank\", \"wald\", \"likelihood\"), ties = c(\"efron\", \"breslow\", \"exact\"), conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for Cox-PH model — control_coxph","text":"pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) string specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for Cox-PH model — control_coxph","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for Cox regression — control_coxreg","title":"Control function for Cox regression — control_coxreg","text":"Sets list parameters Cox regression fit. Used internally.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for Cox regression — control_coxreg","text":"","code":"control_coxreg( pval_method = c(\"wald\", \"likelihood\"), ties = c(\"exact\", \"efron\", \"breslow\"), conf_level = 0.95, interaction = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for Cox regression — control_coxreg","text":"pval_method (string) method used estimation p.values; wald (default) likelihood. ties (string) among exact (equivalent DISCRETE SAS), efron breslow, see survival::coxph(). Note: equivalent SAS EXACT method R. conf_level (proportion) confidence level interval. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for Cox regression — control_coxreg","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for Cox regression — control_coxreg","text":"","code":"control_coxreg() #> $pval_method #> [1] \"wald\" #> #> $ties #> [1] \"exact\" #> #> $conf_level #> [1] 0.95 #> #> $interaction #> [1] FALSE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for incidence rate — control_incidence_rate","title":"Control function for incidence rate — control_incidence_rate","text":"auxiliary function controlling arguments incidence rate, used internally specify details s_incidence_rate().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate( conf_level = 0.95, conf_type = c(\"normal\", \"normal_log\", \"exact\", \"byar\"), input_time_unit = c(\"year\", \"day\", \"week\", \"month\"), num_pt_year = 100 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for incidence rate — control_incidence_rate","text":"conf_level (proportion) confidence level interval. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric(1)) number patient-years use calculating adverse event rates.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for incidence rate — control_incidence_rate","text":"list components names arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for incidence rate — control_incidence_rate","text":"","code":"control_incidence_rate(0.9, \"exact\", \"month\", 100) #> $conf_level #> [1] 0.9 #> #> $conf_type #> [1] \"exact\" #> #> $input_time_unit #> [1] \"month\" #> #> $num_pt_year #> [1] 100 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for g_lineplot() — control_lineplot_vars","title":"Control function for g_lineplot() — control_lineplot_vars","text":"Default values variables parameter g_lineplot function. variable's default value can overwritten variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for g_lineplot() — control_lineplot_vars","text":"","code":"control_lineplot_vars( x = \"AVISIT\", y = \"AVAL\", group_var = \"ARM\", facet_var = NA, paramcd = \"PARAMCD\", y_unit = \"AVALU\", subject_var = \"USUBJID\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for g_lineplot() — control_lineplot_vars","text":"x (string) x-variable name. y (string) y-variable name. group_var (string NA) group variable name. facet_var (string NA) faceting variable name. paramcd (string NA) parameter code variable name. y_unit (string NA) y-axis unit variable name. subject_var (string NA) subject variable name.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for g_lineplot() — control_lineplot_vars","text":"named character vector variable names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_lineplot_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for g_lineplot() — control_lineplot_vars","text":"","code":"control_lineplot_vars() #> x y group_var paramcd y_unit subject_var #> \"AVISIT\" \"AVAL\" \"ARM\" \"PARAMCD\" \"AVALU\" \"USUBJID\" #> facet_var #> NA control_lineplot_vars(group_var = NA) #> x y group_var paramcd y_unit subject_var #> \"AVISIT\" \"AVAL\" NA \"PARAMCD\" \"AVALU\" \"USUBJID\" #> facet_var #> NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for logistic regression model fitting — control_logistic","title":"Control function for logistic regression model fitting — control_logistic","text":"auxiliary function controlling arguments logistic regression models. conf_level refers confidence level used Odds Ratio CIs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for logistic regression model fitting — control_logistic","text":"","code":"control_logistic(response_definition = \"response\", conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for logistic regression model fitting — control_logistic","text":"response_definition (string) definition event terms response. used fitting logistic regression model left hand side formula. Note evaluated expression result either logical vector factor 2 levels. default just \"response\" original response variable used modified . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for logistic regression model fitting — control_logistic","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for logistic regression model fitting — control_logistic","text":"","code":"# Standard options. control_logistic() #> $response_definition #> [1] \"response\" #> #> $conf_level #> [1] 0.95 #> # Modify confidence level. control_logistic(conf_level = 0.9) #> $response_definition #> [1] \"response\" #> #> $conf_level #> [1] 0.9 #> # Use a different response definition. control_logistic(response_definition = \"I(response %in% c('CR', 'PR'))\") #> $response_definition #> [1] \"I(response %in% c('CR', 'PR'))\" #> #> $conf_level #> [1] 0.95 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for risk difference column — control_riskdiff","title":"Control function for risk difference column — control_riskdiff","text":"Sets list parameters use generating risk (proportion) difference column. Used input riskdiff parameter tabulate_rsp_subgroups() tabulate_survival_subgroups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for risk difference column — control_riskdiff","text":"","code":"control_riskdiff( arm_x = NULL, arm_y = NULL, format = \"xx.x (xx.x - xx.x)\", col_label = \"Risk Difference (%) (95% CI)\", pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for risk difference column — control_riskdiff","text":"arm_x (string) name reference arm use risk difference calculations. arm_y (character) names one arms compare reference arm risk difference calculations. new column added value arm_y. format (string function) format label (string) formatting function apply risk difference statistic. See 3d string options list_valid_format_labels() possible format strings. Defaults \"xx.x (xx.x - xx.x)\". col_label (character) labels use rendering risk difference column within table. one comparison arm specified arm_y, default labels specify two arms compared (reference arm vs. comparison arm). pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for risk difference column — control_riskdiff","text":"list items names corresponding arguments.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/control_riskdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for risk difference column — control_riskdiff","text":"","code":"control_riskdiff() #> $arm_x #> NULL #> #> $arm_y #> NULL #> #> $format #> [1] \"xx.x (xx.x - xx.x)\" #> #> $col_label #> [1] \"Risk Difference (%) (95% CI)\" #> #> $pct #> [1] TRUE #> control_riskdiff(arm_x = \"ARM A\", arm_y = \"ARM B\") #> $arm_x #> [1] \"ARM A\" #> #> $arm_y #> [1] \"ARM B\" #> #> $format #> [1] \"xx.x (xx.x - xx.x)\" #> #> $col_label #> [1] \"Risk Difference (%) (95% CI)\" #> #> $pct #> [1] TRUE #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"auxiliary function controlling arguments STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"","code":"control_step( biomarker = NULL, use_percentile = TRUE, bandwidth, degree = 0L, num_points = 39L )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"biomarker (numeric NULL) optional provision numeric biomarker variable, used infer bandwidth, see . use_percentile (flag) TRUE, running windows created according quantiles rather actual values, .e. bandwidth refers percentage data covered window. Suggest TRUE biomarker variable uniformly distributed. bandwidth (numeric(1) NULL) indicating bandwidth window. Depending argument use_percentile, can either length actual-value windows real biomarker scale, percentage windows. use_percentile = TRUE, number 0 1. NULL, treat bandwidth infinity, means one global model fitted. default, 0.25 used percentage windows one quarter range biomarker variable actual-value windows. degree (integer(1)) degree polynomial function biomarker interaction term treatment arm fitted window. 0 (default), biomarker variable included model fitted biomarker window. num_points (integer(1)) number points hazard ratios estimated. smallest number 2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"list components names arguments, except biomarker just used calculate bandwidth case actual biomarker windows requested.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Control function for subgroup treatment effect pattern (STEP) calculations — control_step","text":"","code":"# Provide biomarker values and request actual values to be used, # so that bandwidth is chosen from range. control_step(biomarker = 1:10, use_percentile = FALSE) #> $use_percentile #> [1] FALSE #> #> $bandwidth #> [1] 2.25 #> #> $degree #> [1] 0 #> #> $num_points #> [1] 39 #> # Use a global model with quadratic biomarker interaction term. control_step(bandwidth = NULL, degree = 2) #> $use_percentile #> [1] TRUE #> #> $bandwidth #> NULL #> #> $degree #> [1] 2 #> #> $num_points #> [1] 39 #> # Reduce number of points to be used. control_step(num_points = 10) #> $use_percentile #> [1] TRUE #> #> $bandwidth #> [1] 0.25 #> #> $degree #> [1] 0 #> #> $num_points #> [1] 10 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for survfit models for survival time — control_surv_time","title":"Control function for survfit models for survival time — control_surv_time","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_time(). conf_level refers survival time estimation.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for survfit models for survival time — control_surv_time","text":"","code":"control_surv_time( conf_level = 0.95, conf_type = c(\"plain\", \"log\", \"log-log\"), quantiles = c(0.25, 0.75) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for survfit models for survival time — control_surv_time","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported. quantiles (numeric(2)) vector length two specifying quantiles survival time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_time.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for survfit models for survival time — control_surv_time","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":null,"dir":"Reference","previous_headings":"","what":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"auxiliary function controlling arguments survfit model, typically used internally specify details survfit model s_surv_timepoint(). conf_level refers patient risk estimation time point.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"","code":"control_surv_timepoint( conf_level = 0.95, conf_type = c(\"plain\", \"log\", \"log-log\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"conf_level (proportion) confidence level interval. conf_type (string) confidence interval type. Options \"plain\" (default), \"log\", \"log-log\", see survival::survfit(). Note option \"none\" longer supported.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/control_surv_timepoint.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control function for survfit models for patients' survival rate at time points — control_surv_timepoint","text":"list components names arguments.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Cumulative counts of numeric variable by thresholds — count_cumulative","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"analyze function count_cumulative() creates layout element calculate cumulative counts values numeric variable less , less equal , greater , greater equal user-specified threshold values. function analyzes numeric variable vars threshold values supplied thresholds argument numeric vector. Whether counts include threshold values, whether count values lower higher threshold values can set via include_eq lower_tail parameters, respectively.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"","code":"count_cumulative( lyt, vars, thresholds, lower_tail = TRUE, include_eq = TRUE, var_labels = vars, show_labels = \"visible\", na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_cumulative( x, thresholds, lower_tail = TRUE, include_eq = TRUE, .N_col, ... ) a_count_cumulative( x, thresholds, lower_tail = TRUE, include_eq = TRUE, .N_col, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . thresholds (numeric) vector cutoff values counts. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_cumulative\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"count_cumulative() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_cumulative() table layout. s_count_cumulative() returns named list count_fractions: list thresholds value component, component containing vector count fraction. a_count_cumulative() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"count_cumulative(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_cumulative(): Statistics function produces named list given numeric vector thresholds. a_count_cumulative(): Formatted analysis function used afun count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cumulative counts of numeric variable by thresholds — count_cumulative","text":"","code":"basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_cumulative( vars = \"AGE\", thresholds = c(40, 60) ) %>% build_table(tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> —————————————————————————————————————————————————— #> AGE #> <= 40 52 (75.4%) 58 (79.5%) 41 (70.7%) #> <= 60 69 (100%) 73 (100%) 58 (100%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":null,"dir":"Reference","previous_headings":"","what":"Count number of patients with missed doses by thresholds — count_missed_doses","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"analyze function creates layout element calculate cumulative counts patients number missed doses least equal user-specified threshold values. function analyzes numeric variable vars, variable numbers missed doses, threshold values supplied thresholds argument numeric vector. function assumes every row given data frame corresponds unique patient.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"","code":"count_missed_doses( lyt, vars, thresholds, var_labels = vars, show_labels = \"visible\", na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_nonmissing(x) s_count_missed_doses(x, thresholds, .N_col) a_count_missed_doses(x, thresholds, .N_col)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . thresholds (numeric) minimum number missed doses patients . var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_missed_doses\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"count_missed_doses() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_missed_doses() table layout. s_count_nonmissing() returns statistic n count non-missing values x. s_count_missed_doses() returns statistics n count_fraction one element threshold. a_count_missed_doses() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"count_missed_doses(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_nonmissing(): Statistics function count non-missing values. s_count_missed_doses(): Statistics function count patients missed doses. a_count_missed_doses(): Formatted analysis function used afun count_missed_doses().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_missed_doses.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count number of patients with missed doses by thresholds — count_missed_doses","text":"","code":"library(dplyr) anl <- tern_ex_adsl %>% distinct(STUDYID, USUBJID, ARM) %>% mutate( PARAMCD = \"TNDOSMIS\", PARAM = \"Total number of missed doses during study\", AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE), AVALC = \"\" ) basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_missed_doses(\"AVAL\", thresholds = c(1, 5, 10, 15), var_labels = \"Missed Doses\") %>% build_table(anl, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————— #> Missed Doses #> n 69 73 58 #> At least 1 missed dose 69 (100%) 69 (94.5%) 55 (94.8%) #> At least 5 missed doses 57 (82.6%) 55 (75.3%) 50 (86.2%) #> At least 10 missed doses 40 (58%) 40 (54.8%) 32 (55.2%) #> At least 15 missed doses 26 (37.7%) 25 (34.2%) 13 (22.4%)"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences — count_occurrences","title":"Count occurrences — count_occurrences","text":"analyze function count_occurrences() creates layout element calculate occurrence counts patients. function analyzes variable(s) supplied vars returns table occurrence counts unique value (level) variable(s). variable (variables) must non-numeric. id variable used indicate unique subject identifiers (defaults USUBJID). multiple occurrences value recorded patient, value counted . summarize function summarize_occurrences() performs function count_occurrences() except creates content rows, data rows, summarize current table row/column context operates level latest row split root table row splits occurred.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences — count_occurrences","text":"","code":"count_occurrences( lyt, vars, id = \"USUBJID\", drop = TRUE, var_labels = vars, show_labels = \"hidden\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction_fixed_dp\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) summarize_occurrences( lyt, var, id = \"USUBJID\", drop = TRUE, riskdiff = FALSE, na_str = default_na_str(), ..., .stats = \"count_fraction_fixed_dp\", .formats = NULL, .indent_mods = NULL, .labels = NULL ) s_count_occurrences( df, denom = c(\"N_col\", \"n\"), .N_col, .df_row, drop = TRUE, .var = \"MHDECOD\", id = \"USUBJID\" ) a_count_occurrences( df, labelstr = \"\", id = \"USUBJID\", denom = c(\"N_col\", \"n\"), drop = TRUE, .N_col, .var = NULL, .df_row = NULL, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, na_str = default_na_str() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences — count_occurrences","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . id (string) subject variable name. drop (flag) whether non-appearing occurrence levels dropped resulting table. Note case remaining occurrence levels table sorted alphabetically. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_occurrences\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. denom (string) choice denominator patient proportions. Can : N_col: total number patients column across rows n: number patients occurrences .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .df_row (data.frame) data frame across columns given row split. .var, var (string) single variable name passed rtables requested statistics function. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences — count_occurrences","text":"count_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences() table layout. summarize_occurrences() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences() table layout. s_count_occurrences() returns list : count: list counts one element per occurrence. count_fraction: list counts fractions one element per occurrence. fraction: list numerators denominators one element per occurrence. a_count_occurrences() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count occurrences — count_occurrences","text":"count_occurrences(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_occurrences(): Statistics function counts number patients report occurrence. a_count_occurrences(): Formatted analysis function used afun count_occurrences().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count occurrences — count_occurrences","text":"default, occurrences appear given row split dropped table occurrences table sorted alphabetically per row split. Therefore, corresponding layout needs use split_fun = drop_split_levels split_rows_by calls. Use drop = FALSE like show occurrences.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences — count_occurrences","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c( 1, 1, 2, 4, 4, 4, 6, 6, 6, 7, 7, 8 )), MHDECOD = c( \"MH1\", \"MH2\", \"MH1\", \"MH1\", \"MH1\", \"MH3\", \"MH2\", \"MH2\", \"MH3\", \"MH1\", \"MH2\", \"MH4\" ), ARM = rep(c(\"A\", \"B\"), each = 6), SEX = c(\"F\", \"F\", \"M\", \"M\", \"M\", \"M\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\") ) df_adsl <- df %>% select(USUBJID, ARM) %>% unique() # Create table layout lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences(vars = \"MHDECOD\", .stats = c(\"count_fraction\")) # Apply table layout to data and produce `rtable` object tbl <- lyt %>% build_table(df, alt_counts_df = df_adsl) %>% prune_table() tbl #> A B #> (N=3) (N=3) #> ——————————————————————————— #> MH1 3 (100%) 1 (33.3%) #> MH2 1 (33.3%) 2 (66.7%) #> MH3 1 (33.3%) 1 (33.3%) #> MH4 0 1 (33.3%) # Layout creating function with custom format. basic_table() %>% add_colcounts() %>% split_rows_by(\"SEX\", child_labels = \"visible\") %>% summarize_occurrences( var = \"MHDECOD\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> ————————————————— #> F #> MH1 1 (16.7%) #> MH2 2 (33.3%) #> MH3 1 (16.7%) #> MH4 1 (16.7%) #> M #> MH1 3 (50.0%) #> MH2 1 (16.7%) #> MH3 1 (16.7%) # Count unique occurrences per subject. s_count_occurrences( df, .N_col = 4L, .df_row = df, .var = \"MHDECOD\", id = \"USUBJID\" ) #> $count #> $count$MH1 #> [1] 4 #> #> $count$MH2 #> [1] 3 #> #> $count$MH3 #> [1] 2 #> #> $count$MH4 #> [1] 1 #> #> #> $count_fraction #> $count_fraction$MH1 #> [1] 4 1 #> #> $count_fraction$MH2 #> [1] 3.00 0.75 #> #> $count_fraction$MH3 #> [1] 2.0 0.5 #> #> $count_fraction$MH4 #> [1] 1.00 0.25 #> #> #> $fraction #> $fraction$MH1 #> num denom #> 4 4 #> #> $fraction$MH2 #> num denom #> 3 4 #> #> $fraction$MH3 #> num denom #> 2 4 #> #> $fraction$MH4 #> num denom #> 1 4 #> #> a_count_occurrences( df, .N_col = 4L, .df_row = df, .var = \"MHDECOD\", id = \"USUBJID\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 MH1 4 0 MH1 #> 2 MH2 3 0 MH2 #> 3 MH3 2 0 MH3 #> 4 MH4 1 0 MH4 #> 5 MH1 4 (100%) 0 MH1 #> 6 MH2 3 (75%) 0 MH2 #> 7 MH3 2 (50%) 0 MH3 #> 8 MH4 1 (25%) 0 MH4 #> 9 MH1 4 (100%) 0 MH1 #> 10 MH2 3 (75.0%) 0 MH2 #> 11 MH3 2 (50.0%) 0 MH3 #> 12 MH4 1 (25.0%) 0 MH4 #> 13 MH1 4/4 (100.0%) 0 MH1 #> 14 MH2 3/4 (75.0%) 0 MH2 #> 15 MH3 2/4 (50.0%) 0 MH3 #> 16 MH4 1/4 (25.0%) 0 MH4"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Count occurrences by grade — count_occurrences_by_grade","title":"Count occurrences by grade — count_occurrences_by_grade","text":"analyze function count_occurrences_by_grade() creates layout element calculate occurrence counts grade. function analyzes primary analysis variable var indicates toxicity grades. id variable used indicate unique subject identifiers (defaults USUBJID). user can also supply list custom groups grades analyze via grade_groups parameter. remove_single argument remove single grades analysis grade groups analyzed. multiple grades recorded one patient highest grade level counted. summarize function summarize_occurrences_by_grade() performs function count_occurrences_by_grade() except creates content rows, data rows, summarize current table row/column context operates level latest row split root table row splits occurred.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count occurrences by grade — count_occurrences_by_grade","text":"","code":"count_occurrences_by_grade( lyt, var, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, var_labels = var, show_labels = \"default\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = var, .stats = NULL, .formats = NULL, .indent_mods = NULL, .labels = NULL ) summarize_occurrences_by_grade( lyt, var, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, na_str = default_na_str(), ..., .stats = NULL, .formats = NULL, .indent_mods = NULL, .labels = NULL ) s_count_occurrences_by_grade( df, .var, .N_col, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, labelstr = \"\" ) a_count_occurrences_by_grade( df, .var, .N_col, id = \"USUBJID\", grade_groups = list(), remove_single = TRUE, only_grade_groups = FALSE, labelstr = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count occurrences by grade — count_occurrences_by_grade","text":"lyt (PreDataTableLayouts) layout analyses added . id (string) subject variable name. grade_groups (named list character) list containing groupings grades. remove_single (flag)TRUE include elements one-element grade groups output list; case grade groups names included output. only_grade_groups set TRUE argument ignored. only_grade_groups (flag) whether specified grade groups included, individual grade rows removed (TRUE), grades grade groups displayed (FALSE). var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_occurrences_by_grade\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. .labels (named character) labels statistics (without indent). df (data.frame) data set containing analysis variables. .var, var (string) single variable name passed rtables requested statistics function. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count occurrences by grade — count_occurrences_by_grade","text":"count_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_occurrences_by_grade() table layout. summarize_occurrences_by_grade() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_occurrences_by_grade() table layout. s_count_occurrences_by_grade() returns list counts fractions one element per grade level grade level grouping. a_count_occurrences_by_grade() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count occurrences by grade — count_occurrences_by_grade","text":"count_occurrences_by_grade(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function can take content function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function counts number patients highest grade. a_count_occurrences_by_grade(): Formatted analysis function used afun count_occurrences_by_grade().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_occurrences_by_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count occurrences by grade — count_occurrences_by_grade","text":"","code":"library(dplyr) df <- data.frame( USUBJID = as.character(c(1:6, 1)), ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\", \"A\"), levels = c(\"A\", \"B\")), AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)), AESEV = factor( x = c(\"MILD\", \"MODERATE\", \"SEVERE\", \"MILD\", \"MILD\", \"MODERATE\", \"SEVERE\"), levels = c(\"MILD\", \"MODERATE\", \"SEVERE\") ), stringsAsFactors = FALSE ) df_adsl <- df %>% select(USUBJID, ARM) %>% unique() # Layout creating function with custom format. basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences_by_grade( var = \"AESEV\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> A B #> (N=3) (N=3) #> ———————————————————————————————————————— #> MILD 0.00 (0.00%) 2.00 (66.67%) #> MODERATE 1.00 (33.33%) 1.00 (33.33%) #> SEVERE 2.00 (66.67%) 0.00 (0.00%) # Define additional grade groupings. grade_groups <- list( \"-Any-\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-5\" = c(\"3\", \"4\", \"5\") ) basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_occurrences_by_grade( var = \"AETOXGR\", grade_groups = grade_groups, only_grade_groups = TRUE ) %>% build_table(df, alt_counts_df = df_adsl) #> A B #> (N=3) (N=3) #> ————————————————————————————————— #> -Any- 3 (100%) 3 (100%) #> Grade 1-2 1 (33.3%) 2 (66.7%) #> Grade 3-5 2 (66.7%) 1 (33.3%) # Layout creating function with custom format. basic_table() %>% add_colcounts() %>% split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>% summarize_occurrences_by_grade( var = \"AESEV\", .formats = c(\"count_fraction\" = \"xx.xx (xx.xx%)\") ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> —————————————————————————— #> A #> MILD 0.00 (0.00%) #> MODERATE 1.00 (16.67%) #> SEVERE 2.00 (33.33%) #> B #> MILD 2.00 (33.33%) #> MODERATE 1.00 (16.67%) #> SEVERE 0.00 (0.00%) basic_table() %>% add_colcounts() %>% split_rows_by(\"ARM\", child_labels = \"visible\", nested = TRUE) %>% summarize_occurrences_by_grade( var = \"AETOXGR\", grade_groups = grade_groups ) %>% build_table(df, alt_counts_df = df_adsl) #> all obs #> (N=6) #> ——————————————————————— #> A #> -Any- 3 (50.0%) #> Grade 1-2 1 (16.7%) #> 1 0 #> 2 1 (16.7%) #> Grade 3-5 2 (33.3%) #> 3 2 (33.3%) #> 4 0 #> 5 0 #> B #> -Any- 3 (50.0%) #> Grade 1-2 2 (33.3%) #> 1 1 (16.7%) #> 2 1 (16.7%) #> Grade 3-5 1 (16.7%) #> 3 0 #> 4 1 (16.7%) #> 5 0 s_count_occurrences_by_grade( df, .N_col = 10L, .var = \"AETOXGR\", id = \"USUBJID\", grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> $count_fraction #> $count_fraction$ANY #> [1] 6.0 0.6 #> #> $count_fraction$`1` #> [1] 1.0 0.1 #> #> $count_fraction$`2` #> [1] 2.0 0.2 #> #> $count_fraction$`3` #> [1] 2.0 0.2 #> #> $count_fraction$`4` #> [1] 1.0 0.1 #> #> $count_fraction$`5` #> [1] 0 0 #> #> # We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. afun <- make_afun(a_count_occurrences_by_grade, .ungroup_stats = \"count_fraction\") afun( df, .N_col = 10L, .var = \"AETOXGR\", id = \"USUBJID\", grade_groups = list(\"ANY\" = levels(df$AETOXGR)) ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 ANY 6 (60.0%) 0 ANY #> 2 1 1 (10.0%) 0 1 #> 3 2 2 (20.0%) 0 2 #> 4 3 2 (20.0%) 0 3 #> 5 4 1 (10.0%) 0 4 #> 6 5 0 0 5"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Count patient events in columns — count_patients_events_in_cols","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize function summarize_patients_events_in_cols() creates layout element summarize patient event counts columns. function analyzes elements (events) supplied via filters_list parameter returns row counts number patients event well total numbers patients events. id variable used indicate unique subject identifiers (defaults USUBJID). multiple occurrences event recorded patient, event counted .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count patient events in columns — count_patients_events_in_cols","text":"","code":"summarize_patients_events_in_cols( lyt, id = \"USUBJID\", filters_list = list(), empty_stats = character(), na_str = default_na_str(), ..., .stats = c(\"unique\", \"all\", names(filters_list)), .labels = c(unique = \"Patients (All)\", all = \"Events (All)\", labels_or_names(filters_list)), col_split = TRUE ) s_count_patients_and_multiple_events( df, id, filters_list, empty_stats = character(), labelstr = \"\", custom_label = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count patient events in columns — count_patients_events_in_cols","text":"lyt (PreDataTableLayouts) layout analyses added . id (string) subject variable name. filters_list (named list character) list element list describes one type event describe filters, format s_count_patients_with_event(). label, used column title. empty_stats (character) optional names statistics returned empty corresponding table cells stay blank. na_str (string) string used replace NA empty values output. ... additional arguments lower level functions. .stats (character) statistics select table. Run get_stats(\"summarize_patients_events_in_cols\") see available statistics function, addition added using filters_list. .labels (named character) labels statistics (without indent). col_split (flag) whether columns split. Set FALSE required column split done already earlier layout pipe. df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. custom_label (string NULL) provided labelstr empty used label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize_patients_events_in_cols() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted content rows containing statistics s_count_patients_and_multiple_events() table layout. s_count_patients_and_multiple_events() returns list statistics: unique: number unique patients df. : number rows df. one element name filters_list: number rows df, .e. events, fulfilling filter condition.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count patient events in columns — count_patients_events_in_cols","text":"summarize_patients_events_in_cols(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function counts numbers patients multiple events defined filters. Used analysis function afun summarize_patients_events_in_cols().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_events_in_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count patient events in columns — count_patients_events_in_cols","text":"","code":"df <- data.frame( USUBJID = rep(c(\"id1\", \"id2\", \"id3\", \"id4\"), c(2, 3, 1, 1)), ARM = c(\"A\", \"A\", \"B\", \"B\", \"B\", \"B\", \"A\"), AESER = rep(\"Y\", 7), AESDTH = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"N\"), AEREL = c(\"Y\", \"Y\", \"N\", \"Y\", \"Y\", \"N\", \"Y\"), AEDECOD = c(\"A\", \"A\", \"A\", \"B\", \"B\", \"C\", \"D\"), AEBODSYS = rep(c(\"SOC1\", \"SOC2\", \"SOC3\"), c(3, 3, 1)) ) # `summarize_patients_events_in_cols()` basic_table() %>% summarize_patients_events_in_cols( filters_list = list( related = formatters::with_label(c(AEREL = \"Y\"), \"Events (Related)\"), fatal = c(AESDTH = \"Y\"), fatal_related = c(AEREL = \"Y\", AESDTH = \"Y\") ), custom_label = \"%s Total number of patients and events\" ) %>% build_table(df) #> Patients (All) Events (All) Events (Related) fatal fatal_related #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> %s Total number of patients and events 4 7 5 4 4"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of patients with a particular event — count_patients_with_event","title":"Count the number of patients with a particular event — count_patients_with_event","text":"analyze function count_patients_with_event() creates layout element calculate patient counts user-specified set events. function analyzes primary analysis variable vars indicates unique subject identifiers. Events defined user named vector via filters argument, name corresponds variable value value(s) variable takes event. multiple records event recorded patient, one occurrence counted.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of patients with a particular event — count_patients_with_event","text":"","code":"count_patients_with_event( lyt, vars, filters, riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_count_patients_with_event( df, .var, filters, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) a_count_patients_with_event( df, .var, filters, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of patients with a particular event — count_patients_with_event","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . filters (character) character vector specifying column names flag variables used counting number unique identifiers satisfying conditions. Multiple column names flags accepted format c(\"column_name1\" = \"flag1\", \"column_name2\" = \"flag2\"). Note equality accepted condition. riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_patients_with_event\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) name column contains unique identifier. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of patients with a particular event — count_patients_with_event","text":"count_patients_with_event() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_event() table layout. s_count_patients_with_event() returns count fraction unique identifiers defined event. a_count_patients_with_event() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the number of patients with a particular event — count_patients_with_event","text":"count_patients_with_event(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_patients_with_event(): Statistics function counts number patients defined event occurred. a_count_patients_with_event(): Formatted analysis function used afun count_patients_with_event().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_event.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of patients with a particular event — count_patients_with_event","text":"","code":"# `count_patients_with_event()` lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_values( \"STUDYID\", values = \"AB12345\", .stats = \"count\", .labels = c(count = \"Total AEs\") ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\"), .labels = c(count_fraction = \"Total number of patients with at least one adverse event\"), table_names = \"tbl_all\" ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"), .labels = c(count_fraction = \"Total number of patients with fatal AEs\"), table_names = \"tbl_fatal\" ) %>% count_patients_with_event( \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\", \"AEREL\" = \"Y\"), .labels = c(count_fraction = \"Total number of patients with related fatal AEs\"), .indent_mods = c(count_fraction = 2L), table_names = \"tbl_rel_fatal\" ) build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs 202 177 162 #> Total number of patients with at least one adverse event 59 (100%) 57 (100%) 48 (100%) #> Total number of patients with fatal AEs 28 (47.5%) 31 (54.4%) 20 (41.7%) #> Total number of patients with related fatal AEs 28 (47.5%) 31 (54.4%) 20 (41.7%) # `s_count_patients_with_event()` s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\") ) #> $n #> [1] 164 #> #> $count #> [1] 164 #> #> $count_fraction #> [1] 164 1 #> #> $n_blq #> [1] 0 #> s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\") ) #> $n #> [1] 164 #> #> $count #> [1] 79 #> #> $count_fraction #> [1] 79.0000000 0.4817073 #> #> $n_blq #> [1] 0 #> s_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\", \"AEOUT\" = \"FATAL\"), denom = \"N_col\", .N_col = 456 ) #> $n #> [1] 164 #> #> $count #> [1] 79 #> #> $count_fraction #> [1] 79.0000000 0.1732456 #> #> $n_blq #> [1] 0 #> # `a_count_patients_with_event()` a_count_patients_with_event( tern_ex_adae, .var = \"SUBJID\", filters = c(\"TRTEMFL\" = \"Y\"), .N_col = 100, .N_row = 100 ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 164 0 n #> 2 count 164 0 count #> 3 count_fraction 164 (100%) 0 count_fraction #> 4 n_blq 0 0 n_blq"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":null,"dir":"Reference","previous_headings":"","what":"Count the number of patients with particular flags — count_patients_with_flags","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"analyze function count_patients_with_flags() creates layout element calculate counts patients user-specified flags present. function analyzes primary analysis variable var indicates unique subject identifiers. Flags variables analyze specified user via flag_variables argument, must either take value TRUE (flag present) FALSE (flag absent) record. multiple records flag present patient, one occurrence counted.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"","code":"count_patients_with_flags( lyt, var, flag_variables, flag_labels = NULL, var_labels = var, show_labels = \"hidden\", riskdiff = FALSE, na_str = default_na_str(), nested = TRUE, ..., table_names = paste0(\"tbl_flags_\", var), .stats = \"count_fraction\", .formats = NULL, .indent_mods = NULL ) s_count_patients_with_flags( df, .var, flag_variables, flag_labels = NULL, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) a_count_patients_with_flags( df, .var, flag_variables, flag_labels = NULL, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. flag_variables (character) vector specifying names logical variables analysis dataset used counting number unique identifiers. flag_labels (character) vector labels use flag variables. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". riskdiff (flag) whether risk difference column present. set TRUE, add_riskdiff() must used split_fun prior column split table layout, specifying columns compared. See stat_propdiff_ci() details risk difference calculation. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_patients_with_flags\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) name column contains unique identifier. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"count_patients_with_flags() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_patients_with_flags() table layout. s_count_patients_with_flags() returns count fraction unique identifiers particular flag list statistics n, count, count_fraction, n_blq, one element per flag. a_count_patients_with_flags() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"count_patients_with_flags(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_patients_with_flags(): Statistics function counts number patients particular flag variable TRUE. a_count_patients_with_flags(): Formatted analysis function used afun count_patients_with_flags().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"flag_labels specified, variables labels extracted df. variables labeled, variable names used instead. Alternatively, named vector can supplied flag_variables within name-value pair name corresponds variable name value label use variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/count_patients_with_flags.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count the number of patients with particular flags — count_patients_with_flags","text":"","code":"library(dplyr) # Add labelled flag variables to analysis dataset. adae <- tern_ex_adae %>% mutate( fl1 = TRUE %>% with_label(\"Total AEs\"), fl2 = (TRTEMFL == \"Y\") %>% with_label(\"Total number of patients with at least one adverse event\"), fl3 = (TRTEMFL == \"Y\" & AEOUT == \"FATAL\") %>% with_label(\"Total number of patients with fatal AEs\"), fl4 = (TRTEMFL == \"Y\" & AEOUT == \"FATAL\" & AEREL == \"Y\") %>% with_label(\"Total number of patients with related fatal AEs\") ) # `count_patients_with_flags()` lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% count_patients_with_flags( \"SUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"), denom = \"N_col\" ) build_table(lyt2, adae, alt_counts_df = tern_ex_adsl) #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> Total AEs 59 (85.5%) 57 (78.1%) 48 (82.8%) #> Total number of patients with at least one adverse event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> Total number of patients with fatal AEs 28 (40.6%) 31 (42.5%) 20 (34.5%) #> Total number of patients with related fatal AEs 28 (40.6%) 31 (42.5%) 20 (34.5%) # `s_count_patients_with_flags()` s_count_patients_with_flags( adae, \"SUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\"), denom = \"N_col\", .N_col = 1000 ) #> $n #> $n$`Total AEs` #> [1] 164 #> #> $n$`Total number of patients with at least one adverse event` #> [1] 164 #> #> $n$`Total number of patients with fatal AEs` #> [1] 164 #> #> $n$`Total number of patients with related fatal AEs` #> [1] 164 #> #> #> $count #> $count$`Total AEs` #> [1] 164 #> #> $count$`Total number of patients with at least one adverse event` #> [1] 164 #> #> $count$`Total number of patients with fatal AEs` #> [1] 79 #> #> $count$`Total number of patients with related fatal AEs` #> [1] 79 #> #> #> $count_fraction #> $count_fraction$`Total AEs` #> [1] 164.000 0.164 #> #> $count_fraction$`Total number of patients with at least one adverse event` #> [1] 164.000 0.164 #> #> $count_fraction$`Total number of patients with fatal AEs` #> [1] 79.000 0.079 #> #> $count_fraction$`Total number of patients with related fatal AEs` #> [1] 79.000 0.079 #> #> #> $n_blq #> $n_blq$`Total AEs` #> [1] 0 #> #> $n_blq$`Total number of patients with at least one adverse event` #> [1] 0 #> #> $n_blq$`Total number of patients with fatal AEs` #> [1] 0 #> #> $n_blq$`Total number of patients with related fatal AEs` #> [1] 0 #> #> # We need to ungroup `count_fraction` first so that the `rtables` formatting # function `format_count_fraction()` can be applied correctly. # `a_count_patients_with_flags()` afun <- make_afun(a_count_patients_with_flags, .stats = \"count_fraction\", .ungroup_stats = \"count_fraction\" ) afun( adae, .N_col = 10L, .N_row = 10L, .var = \"USUBJID\", flag_variables = c(\"fl1\", \"fl2\", \"fl3\", \"fl4\") ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell #> 1 Total AEs 164 (100%) #> 2 Total number of patients with at least one adverse event 164 (100%) #> 3 Total number of patients with fatal AEs 79 (48.2%) #> 4 Total number of patients with related fatal AEs 79 (48.2%) #> indent_mod row_label #> 1 0 Total AEs #> 2 0 Total number of patients with at least one adverse event #> 3 0 Total number of patients with fatal AEs #> 4 0 Total number of patients with related fatal AEs"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Count specific values — count_values","title":"Count specific values — count_values","text":"analyze function count_values() creates layout element calculate counts specific values within variable interest. function analyzes one variables interest supplied vector vars. Values count variable(s) vars can given vector via values argument. One row counts generated variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count specific values — count_values","text":"","code":"count_values( lyt, vars, values, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = \"count_fraction\", .formats = NULL, .labels = c(count_fraction = paste(values, collapse = \", \")), .indent_mods = NULL ) s_count_values( x, values, na.rm = TRUE, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") ) # S3 method for class 'character' s_count_values(x, values = \"Y\", na.rm = TRUE, ...) # S3 method for class 'factor' s_count_values(x, values = \"Y\", ...) # S3 method for class 'logical' s_count_values(x, values = TRUE, ...) a_count_values( x, values, na.rm = TRUE, .N_col, .N_row, denom = c(\"n\", \"N_row\", \"N_col\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count specific values — count_values","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . values (character) specific values counted. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"count_values\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables. .N_row (integer(1)) row-wise N (row group count) group observations analyzed (.e. column-based subsetting) typically passed rtables. denom (string) choice denominator proportion. Options : n: number values row column intersection. N_row: total number values row across columns. N_col: total number values column across rows.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count specific values — count_values","text":"count_values() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_count_values() table layout. s_count_values() returns output s_summary() specified values non-numeric variable. a_count_values() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Count specific values — count_values","text":"count_values(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_count_values(): S3 generic function count values. s_count_values(character): Method character class. s_count_values(factor): Method factor class. makes automatic conversion character forwards method characters. s_count_values(logical): Method logical class. a_count_values(): Formatted analysis function used afun count_values().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Count specific values — count_values","text":"factor variables, s_count_values checks whether values included levels x fails otherwise. count_values(), variable labels shown one element vars, otherwise hidden.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/count_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count specific values — count_values","text":"","code":"# `count_values` basic_table() %>% count_values(\"Species\", values = \"setosa\") %>% build_table(iris) #> all obs #> ———————————————————— #> setosa 50 (33.33%) # `s_count_values.character` s_count_values(x = c(\"a\", \"b\", \"a\"), values = \"a\") #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> s_count_values(x = c(\"a\", \"b\", \"a\", NA, NA), values = \"b\", na.rm = FALSE) #> $n #> [1] 5 #> #> $count #> [1] 1 #> #> $count_fraction #> [1] 1.0 0.2 #> #> $n_blq #> [1] 0 #> # `s_count_values.factor` s_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\") #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> # `s_count_values.logical` s_count_values(x = c(TRUE, FALSE, TRUE)) #> $n #> [1] 3 #> #> $count #> [1] 2 #> #> $count_fraction #> [1] 2.0000000 0.6666667 #> #> $n_blq #> [1] 0 #> # `a_count_values` a_count_values(x = factor(c(\"a\", \"b\", \"a\")), values = \"a\", .N_col = 10, .N_row = 10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 3 0 n #> 2 count 2 0 count #> 3 count_fraction 2 (66.67%) 0 count_fraction #> 4 n_blq 0 0 n_blq"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox proportional hazards regression — cox_regression","title":"Cox proportional hazards regression — cox_regression","text":"Fits Cox regression model estimates hazard ratio describe effect size survival analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox proportional hazards regression — cox_regression","text":"","code":"summarize_coxreg( lyt, variables, control = control_coxreg(), at = list(), multivar = FALSE, common_var = \"STUDYID\", .stats = c(\"n\", \"hr\", \"ci\", \"pval\", \"pval_inter\"), .formats = c(n = \"xx\", hr = \"xx.xx\", ci = \"(xx.xx, xx.xx)\", pval = \"x.xxxx | (<0.0001)\", pval_inter = \"x.xxxx | (<0.0001)\"), varlabels = NULL, .indent_mods = NULL, na_str = \"\", .section_div = NA_character_ ) s_coxreg(model_df, .stats, .which_vars = \"all\", .var_nms = NULL) a_coxreg( df, labelstr, eff = FALSE, var_main = FALSE, multivar = FALSE, variables, at = list(), control = control_coxreg(), .spl_context, .stats, .formats, .indent_mods = NULL, na_str = \"\", cache_env = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox proportional hazards regression — cox_regression","text":"lyt (PreDataTableLayouts) layout analyses added . variables (named list string) list additional analysis variables. control (list) list parameters returned helper function control_coxreg(). (list numeric) candidate covariate numeric, use specify value covariate effect estimated. multivar (flag) whether multivariate Cox regression run (defaults FALSE), otherwise univariate Cox regression run. common_var (string) name factor variable dataset takes value rows. created pre-processing variable currently exists. .stats (character) names statistics reported among: n: number observations (univariate ) hr: hazard ratio ci: confidence interval pval: p-value treatment effect pval_inter: p-value interaction effect treatment covariate (univariate ) .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. varlabels (list) named list corresponds names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. na_str (string) custom string replace NA values . Defaults \"\". .section_div (string NA) string repeated section divider sections. Defaults NA section divider. vector two strings given, first used treatment covariate sections second different covariates. model_df (data.frame) contains resulting model fit fit_coxreg function tidying applied via broom::tidy(). .which_vars (character) rows statistics returned given model. Defaults \"\". options include \"var_main\" main effects, \"inter\" interaction effects, \"multi_lvl\" multivariate model covariate level rows. .which_vars \"\", specific variables can selected specifying .var_nms. .var_nms (character) term value rows df .stats returned. Typically name variable. using variable labels, var vector desired variable name variable label order see .stats related variable. .which_vars \"var_main\", .var_nms variable name. df (data.frame) data set containing analysis variables. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. eff (flag) whether treatment effect calculated. Defaults FALSE. var_main (flag) whether main effects calculated. Defaults FALSE. .spl_context (data.frame) gives information ancestor split states passed rtables. cache_env (environment) environment object used cache regression model order avoid repeatedly fitting model every row table. Defaults NULL (caching).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox proportional hazards regression — cox_regression","text":"summarize_coxreg() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add Cox regression table containing chosen statistics table layout. s_coxreg() returns selected statistic Cox regression model selected variable(s). a_coxreg() returns formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox proportional hazards regression — cox_regression","text":"Cox models commonly used methods estimate magnitude effect survival analysis. assumes proportional hazards: ratio hazards groups (e.g., two arms) constant time. ratio referred \"hazard ratio\" (HR) one commonly reported metrics describe effect size survival analysis (NEST Team, 2020).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox proportional hazards regression — cox_regression","text":"summarize_coxreg(): Layout-creating function creates Cox regression summary table layout. function wrapper several rtables layouting functions. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups(). s_coxreg(): Statistics function transforms results tabulated fit_coxreg_univar() fit_coxreg_multivar() list. a_coxreg(): Analysis function used afun rtables::analyze() cfun rtables::summarize_row_groups() within summarize_coxreg().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox proportional hazards regression — cox_regression","text":"","code":"library(survival) # Testing dataset [survival::bladder]. set.seed(1, kind = \"Mersenne-Twister\") dta_bladder <- with( data = bladder[bladder$enum < 5, ], tibble::tibble( TIME = stop, STATUS = event, ARM = as.factor(rx), COVAR1 = as.factor(enum) %>% formatters::with_label(\"A Covariate Label\"), COVAR2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) %>% formatters::with_label(\"Sex (F/M)\") ) ) dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) dta_bladder$STUDYID <- factor(\"X\") u1_variables <- list( time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) u2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) m1_variables <- list( time = \"TIME\", event = \"STATUS\", arm = \"ARM\", covariates = c(\"COVAR1\", \"COVAR2\") ) m2_variables <- list(time = \"TIME\", event = \"STATUS\", covariates = c(\"COVAR1\", \"COVAR2\")) # summarize_coxreg result_univar <- basic_table() %>% summarize_coxreg(variables = u1_variables) %>% build_table(dta_bladder) result_univar #> n Hazard Ratio 95% CI p-value #> ————————————————————————————————————————————————————————————————— #> Treatment: #> 2 vs control (1) 340 0.64 (0.43, 0.94) 0.0242 #> Covariate: #> A Covariate Label 340 0.61 (0.41, 0.90) 0.0126 #> Sex (F/M) 340 0.62 (0.42, 0.92) 0.0182 result_univar_covs <- basic_table() %>% summarize_coxreg( variables = u2_variables, ) %>% build_table(dta_bladder) result_univar_covs #> Hazard Ratio 95% CI p-value #> ——————————————————————————————————————————————————————————————————————————— #> Covariate: #> A Covariate Label (reference = 1) <0.0001 #> 2 0.45 (0.28, 0.71) 0.0007 #> 3 0.31 (0.19, 0.52) <0.0001 #> 4 0.18 (0.10, 0.33) <0.0001 #> Sex (F/M) (reference = F) #> M 1.33 (0.91, 1.94) 0.1414 result_multivar <- basic_table() %>% summarize_coxreg( variables = m1_variables, multivar = TRUE, ) %>% build_table(dta_bladder) result_multivar #> Hazard Ratio 95% CI p-value #> ——————————————————————————————————————————————————————————————————————————— #> Treatment: #> ARM (reference = 1) #> 2 0.61 (0.41, 0.90) 0.0123 #> Covariate: #> A Covariate Label (reference = 1) <0.0001 #> 2 0.46 (0.28, 0.73) 0.0011 #> 3 0.31 (0.18, 0.51) <0.0001 #> 4 0.18 (0.10, 0.33) <0.0001 #> Sex (F/M) (reference = F) #> M 1.29 (0.88, 1.89) 0.1911 result_multivar_covs <- basic_table() %>% summarize_coxreg( variables = m2_variables, multivar = TRUE, varlabels = c(\"Covariate 1\", \"Covariate 2\") # custom labels ) %>% build_table(dta_bladder) result_multivar_covs #> Hazard Ratio 95% CI p-value #> ————————————————————————————————————————————————————————————————————— #> Covariate: #> Covariate 1 (reference = 1) <0.0001 #> 2 0.46 (0.29, 0.74) 0.0012 #> 3 0.31 (0.19, 0.52) <0.0001 #> 4 0.19 (0.10, 0.34) <0.0001 #> Covariate 2 (reference = F) #> M 1.29 (0.88, 1.88) 0.1958 # s_coxreg # Univariate univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder) df1 <- broom::tidy(univar_model) s_coxreg(model_df = df1, .stats = \"hr\") #> $hr #> $hr$`2 vs control (1)` #> [1] 0.6386426 #> #> #> $hr #> $hr$`A Covariate Label` #> [1] 0.607037 #> #> #> $hr #> $hr$`Sex (F/M)` #> [1] 0.6242738 #> #> # Univariate with interactions univar_model_inter <- fit_coxreg_univar( variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder ) df1_inter <- broom::tidy(univar_model_inter) s_coxreg(model_df = df1_inter, .stats = \"hr\", .which_vars = \"inter\", .var_nms = \"COVAR1\") #> $hr #> $hr$` 1` #> [1] 0.6284569 #> #> $hr$` 2` #> [1] 0.5806499 #> #> $hr$` 3` #> [1] 0.5486103 #> #> $hr$` 4` #> [1] 0.6910725 #> #> # Univariate without treatment arm - only \"COVAR2\" covariate effects univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder) df1_covs <- broom::tidy(univar_covs_model) s_coxreg(model_df = df1_covs, .stats = \"hr\", .var_nms = c(\"COVAR2\", \"Sex (F/M)\")) #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #> #> #> $hr #> $hr$M #> [1] 1.3271 #> #> # Multivariate. multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder) df2 <- broom::tidy(multivar_model) s_coxreg(model_df = df2, .stats = \"pval\", .which_vars = \"var_main\", .var_nms = \"COVAR1\") #> $pval #> $pval$`A Covariate Label (reference = 1)` #> [1] 7.209956e-09 #> #> s_coxreg( model_df = df2, .stats = \"pval\", .which_vars = \"multi_lvl\", .var_nms = c(\"COVAR1\", \"A Covariate Label\") ) #> $pval #> $pval$`2` #> [1] 0.001120332 #> #> $pval$`3` #> [1] 6.293725e-06 #> #> $pval$`4` #> [1] 3.013875e-08 #> #> # Multivariate without treatment arm - only \"COVAR1\" main effect multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder) df2_covs <- broom::tidy(multivar_covs_model) s_coxreg(model_df = df2_covs, .stats = \"hr\") #> $hr #> $hr$`2` #> [1] 0.4600728 #> #> $hr$`3` #> [1] 0.3100455 #> #> $hr$`4` #> [1] 0.1854177 #> #> #> $hr #> $hr$`A Covariate Label (reference = 1)` #> numeric(0) #> #> #> $hr #> $hr$`Sex (F/M) (reference = F)` #> numeric(0) #> #> #> $hr #> $hr$M #> [1] 1.285406 #> #> a_coxreg( df = dta_bladder, labelstr = \"Label 1\", variables = u1_variables, .spl_context = list(value = \"COVAR1\"), .stats = \"n\", .formats = \"xx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Label 1 340 0 Label 1 a_coxreg( df = dta_bladder, labelstr = \"\", variables = u1_variables, .spl_context = list(value = \"COVAR2\"), .stats = \"pval\", .formats = \"xx.xxxx\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Sex (F/M) 0.0182 0 Sex (F/M)"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":null,"dir":"Reference","previous_headings":"","what":"Cox regression helper function for interactions — cox_regression_inter","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Test estimate effect treatment interaction covariate. effect estimated HR tested treatment given level covariate, comparison treatment control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cox regression helper function for interactions — cox_regression_inter","text":"","code":"h_coxreg_inter_effect(x, effect, covar, mod, label, control, ...) # S3 method for class 'numeric' h_coxreg_inter_effect(x, effect, covar, mod, label, control, at, ...) # S3 method for class 'factor' h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...) # S3 method for class 'character' h_coxreg_inter_effect(x, effect, covar, mod, label, control, data, ...) h_coxreg_extract_interaction(effect, covar, mod, data, at, control) h_coxreg_inter_estimations( variable, given, lvl_var, lvl_given, mod, conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cox regression helper function for interactions — cox_regression_inter","text":"x (numeric factor) values covariate tested. effect (string) name effect tested estimated. covar (string) name covariate model. mod (coxph) fitted Cox regression model (see survival::coxph()). label (string) label returned term_label. control (list) list controls returned control_coxreg(). ... see methods. (list) list items named covariate, every item vector levels interaction estimated. data (data.frame) data frame model fit. variable, given (string) name variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cox regression helper function for interactions — cox_regression_inter","text":"h_coxreg_inter_effect() returns data.frame covariate interaction effects consisting following variables: effect, term, term_label, level, n, hr, lcl, ucl, pval, pval_inter. h_coxreg_extract_interaction() returns result interaction test estimated values. interaction, h_coxreg_univar_extract() applied instead. h_coxreg_inter_estimations() returns list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female) model abbreviated: y ~ Arm + Sex + Arm:Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) estimation Hazard Ratio arm C/sex M given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5). interaction coefficient deduced b2 + b5 standard error obtained $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Cox regression helper function for interactions — cox_regression_inter","text":"h_coxreg_inter_effect(): S3 generic helper function determine interaction effect. h_coxreg_inter_effect(numeric): Method numeric class. Estimates interaction numeric covariate. h_coxreg_inter_effect(factor): Method factor class. Estimate interaction factor covariate. h_coxreg_inter_effect(character): Method character class. Estimate interaction character covariate. makes automatic conversion factor forwards method factors. h_coxreg_extract_interaction(): higher level function get results interaction test estimated values. h_coxreg_inter_estimations(): Hazard ratio estimation interactions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cox regression helper function for interactions — cox_regression_inter","text":"Automatic conversion character factor guarantee results can generated correctly. therefore better always pre-process dataset factors manually created character variables passing dataset rtables::build_table().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cox_regression_inter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cox regression helper function for interactions — cox_regression_inter","text":"","code":"library(survival) set.seed(1, kind = \"Mersenne-Twister\") # Testing dataset [survival::bladder]. dta_bladder <- with( data = bladder[bladder$enum < 5, ], data.frame( time = stop, status = event, armcd = as.factor(rx), covar1 = as.factor(enum), covar2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) plot( survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder), lty = 2:4, xlab = \"Months\", col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") ) mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) h_coxreg_extract_interaction( mod = mod, effect = \"armcd\", covar = \"covar1\", data = dta_bladder, control = control_coxreg() ) #> effect term term_label level n hr #> 1 Covariate: covar1 A Covariate Label 340 NA #> armcd2/covar11 Covariate: covar1 1 1 NA 0.6341111 #> armcd2/covar12 Covariate: covar1 2 2 NA 0.5845305 #> armcd2/covar13 Covariate: covar1 3 3 NA 0.5507703 #> armcd2/covar14 Covariate: covar1 4 4 NA 0.6910643 #> lcl ucl pval pval_inter #> 1 NA NA 0.1302825 0.988245 #> armcd2/covar11 0.3514676 1.144051 NA NA #> armcd2/covar12 0.2716689 1.257692 NA NA #> armcd2/covar13 0.2244668 1.351415 NA NA #> armcd2/covar14 0.2315248 2.062715 NA NA mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder) result <- h_coxreg_inter_estimations( variable = \"armcd\", given = \"covar1\", lvl_var = levels(dta_bladder$armcd), lvl_given = levels(dta_bladder$covar1), mod = mod, conf_level = .95 ) result #> $armcd2 #> coef se(coef) hr lcl ucl #> armcd2/covar11 -0.4555312 0.3010803 0.6341111 0.3514676 1.144051 #> armcd2/covar12 -0.5369464 0.3909383 0.5845305 0.2716689 1.257692 #> armcd2/covar13 -0.5964375 0.4579624 0.5507703 0.2244668 1.351415 #> armcd2/covar14 -0.3695225 0.5579418 0.6910643 0.2315248 2.062715 #> #> attr(,\"details\") #> [1] \"Estimations of armcd hazard ratio given the level of covar1 compared to armcd level 1.\""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":null,"dir":"Reference","previous_headings":"","what":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"cuts numeric vector sample quantile bins.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"","code":"cut_quantile_bins( x, probs = c(0.25, 0.5, 0.75), labels = NULL, type = 7, ordered = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"x (numeric) continuous variable values cut quantile bins. may contain NA values, used quantile calculations, included return vector. probs (numeric) probabilities identifying quantiles. sorted vector unique proportion values, .e. 0 1, boundaries 0 1 must included. labels (character) unique labels quantile bins. n probabilities probs, must n + 1 long. type (integer(1)) type quantiles use, see stats::quantile() details. ordered (flag) result ordered factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"factor variable appropriately-labeled bins levels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"Intervals closed right side. , first bin interval [-Inf, q1] q1 first quantile, second bin (q1, q2], etc., last bin (qn, +Inf] qn last quantile.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/cut_quantile_bins.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cut numeric vector into empirical quantile bins — cut_quantile_bins","text":"","code":"# Default is to cut into quartile bins. cut_quantile_bins(cars$speed) #> [1] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] #> [7] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] #> [13] [0%,25%] [0%,25%] [0%,25%] (25%,50%] (25%,50%] (25%,50%] #> [19] (25%,50%] (25%,50%] (25%,50%] (25%,50%] (25%,50%] (25%,50%] #> [25] (25%,50%] (25%,50%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] #> [31] (50%,75%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] (50%,75%] #> [37] (50%,75%] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [43] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [49] (75%,100%] (75%,100%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] # Use custom quantiles. cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88)) #> [1] [0%,10%] [0%,10%] [0%,10%] [0%,10%] [0%,10%] (10%,20%] #> [7] (10%,20%] (10%,20%] (10%,20%] (10%,20%] (10%,20%] (20%,60%] #> [13] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [19] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [25] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] (20%,60%] #> [31] (20%,60%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] #> [37] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] (60%,88%] #> [43] (60%,88%] (60%,88%] (88%,100%] (88%,100%] (88%,100%] (88%,100%] #> [49] (88%,100%] (88%,100%] #> Levels: [0%,10%] < (10%,20%] < (20%,60%] < (60%,88%] < (88%,100%] # Use custom labels. cut_quantile_bins(cars$speed, labels = paste0(\"Q\", 1:4)) #> [1] Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q1 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 Q2 #> [26] Q2 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q3 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 Q4 #> Levels: Q1 < Q2 < Q3 < Q4 # NAs are preserved in result factor. ozone_binned <- cut_quantile_bins(airquality$Ozone) which(is.na(ozone_binned)) #> [1] 5 10 25 26 27 32 33 34 35 36 37 39 42 43 45 46 52 53 54 #> [20] 55 56 57 58 59 60 61 65 72 75 83 84 102 103 107 115 119 150 # So you might want to make these explicit. explicit_na(ozone_binned) #> [1] (50%,75%] (50%,75%] [0%,25%] [0%,25%] (25%,50%] #> [7] (25%,50%] (25%,50%] [0%,25%] [0%,25%] [0%,25%] #> [13] [0%,25%] [0%,25%] [0%,25%] [0%,25%] (50%,75%] [0%,25%] #> [19] (25%,50%] [0%,25%] [0%,25%] [0%,25%] [0%,25%] (50%,75%] #> [25] (25%,50%] (50%,75%] (75%,100%] #> [31] (50%,75%] #> [37] (25%,50%] (75%,100%] (50%,75%] #> [43] (25%,50%] (25%,50%] (50%,75%] #> [49] (25%,50%] [0%,25%] [0%,25%] #> [55] #> [61] (75%,100%] (50%,75%] (50%,75%] (75%,100%] #> [67] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [73] [0%,25%] (25%,50%] [0%,25%] (50%,75%] (50%,75%] #> [79] (50%,75%] (75%,100%] (50%,75%] [0%,25%] #> [85] (75%,100%] (75%,100%] (25%,50%] (50%,75%] (75%,100%] (50%,75%] #> [91] (75%,100%] (50%,75%] (50%,75%] [0%,25%] [0%,25%] (75%,100%] #> [97] (50%,75%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [103] (50%,75%] (25%,50%] (75%,100%] (25%,50%] #> [109] (50%,75%] (25%,50%] (25%,50%] (50%,75%] (25%,50%] [0%,25%] #> [115] (50%,75%] (75%,100%] (75%,100%] (75%,100%] #> [121] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] (75%,100%] #> [127] (75%,100%] (50%,75%] (50%,75%] (25%,50%] (25%,50%] (25%,50%] #> [133] (25%,50%] (50%,75%] (25%,50%] (25%,50%] [0%,25%] [0%,25%] #> [139] (50%,75%] [0%,25%] [0%,25%] (25%,50%] [0%,25%] [0%,25%] #> [145] (25%,50%] (50%,75%] [0%,25%] [0%,25%] (25%,50%] #> [151] [0%,25%] [0%,25%] (25%,50%] #> Levels: [0%,25%] < (25%,50%] < (50%,75%] < (75%,100%] < "},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":null,"dir":"Reference","previous_headings":"","what":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Description function produces labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(abnormal)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"abnormal (character) values identifying abnormal range level(s) .var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"Abnormal category labels s_count_abnormal_by_baseline().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_abnormal_by_baseline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description function for s_count_abnormal_by_baseline() — d_count_abnormal_by_baseline","text":"","code":"d_count_abnormal_by_baseline(\"LOW\") #> $not_abnormal #> [1] \"Not low\" #> #> $abnormal #> [1] \"Low\" #> #> $total #> [1] \"Total\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of cumulative count — d_count_cumulative","title":"Description of cumulative count — d_count_cumulative","text":"helper function describes analysis s_count_cumulative().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of cumulative count — d_count_cumulative","text":"","code":"d_count_cumulative(threshold, lower_tail = TRUE, include_eq = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of cumulative count — d_count_cumulative","text":"threshold (numeric(1)) cutoff value threshold count values x. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of cumulative count — d_count_cumulative","text":"Labels s_count_cumulative().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"","code":"d_count_missed_doses(thresholds)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"thresholds (numeric) minimum number missed doses patients .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_count_missed_doses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description function that calculates labels for s_count_missed_doses() — d_count_missed_doses","text":"d_count_missed_doses() returns named character vector labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of standard oncology response — d_onco_rsp_label","title":"Description of standard oncology response — d_onco_rsp_label","text":"Describe oncology response standard way.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of standard oncology response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of standard oncology response — d_onco_rsp_label","text":"x (character) standard oncology codes described.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of standard oncology response — d_onco_rsp_label","text":"Response labels.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_onco_rsp_label.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Description of standard oncology response — d_onco_rsp_label","text":"","code":"d_onco_rsp_label( c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\", \"Missing\", \"\", \"NE/Missing\") ) #> CR PR #> Complete Response (CR) Partial Response (PR) #> SD NON CR/PD #> Stable Disease (SD) Non-CR or Non-PD (NON CR/PD) #> PD NE #> Progressive Disease (PD) Not Evaluable (NE) #> Missing #> Missing #> NE/Missing #> Missing or unevaluable #> 9 Levels: Complete Response (CR) Partial Response (PR) ... # Adding some values not considered in d_onco_rsp_label d_onco_rsp_label( c(\"CR\", \"PR\", \"hello\", \"hi\") ) #> CR PR hello #> Complete Response (CR) Partial Response (PR) hello #> hi #> hi #> Levels: Complete Response (CR) Partial Response (PR) hello hi"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate PK reference dataset — d_pkparam","text":"","code":"d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate PK reference dataset — d_pkparam","text":"data.frame PK parameters.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_pkparam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate PK reference dataset — d_pkparam","text":"","code":"pk_reference_dataset <- d_pkparam()"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the proportion summary — d_proportion","title":"Description of the proportion summary — d_proportion","text":"helper function describes analysis s_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the proportion summary — d_proportion","text":"","code":"d_proportion(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the proportion summary — d_proportion","text":"conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. long (flag) whether long short (default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the proportion summary — d_proportion","text":"String describing analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of method used for proportion comparison — d_proportion_diff","title":"Description of method used for proportion comparison — d_proportion_diff","text":"auxiliary function describes analysis s_proportion_diff().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of method used for proportion comparison — d_proportion_diff","text":"","code":"d_proportion_diff(conf_level, method, long = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of method used for proportion comparison — d_proportion_diff","text":"conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. long (flag) whether long (TRUE) short (FALSE, default) description required.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of method used for proportion comparison — d_proportion_diff","text":"string describing analysis.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"Internal function check variables included tabulate_rsp_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"","code":"d_rsp_subgroups_colvars(vars, conf_level = NULL, method = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_rsp_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for column variables in binary response by subgroup table — d_rsp_subgroups_colvars","text":"list variables tabulate labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"Internal function check variables included tabulate_survival_subgroups() create column labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"","code":"d_survival_subgroups_colvars(vars, conf_level, method, time_unit = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"vars (character) names statistics reported among: n_tot_events: Total number events per group. n_events: Number events per group. n_tot: Total number observations per group. n: Number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. conf_level (proportion) confidence level interval. method (string) p-value method testing hazard ratio = 1. time_unit (string) label unit median survival time. Default NULL skips displaying unit.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"list variables labels tabulate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_survival_subgroups_colvars.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Labels for column variables in survival duration by subgroup table — d_survival_subgroups_colvars","text":"least one n_tot n_tot_events must provided vars.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Description of the difference test between two proportions — d_test_proportion_diff","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"auxiliary function describes analysis s_test_proportion_diff.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"","code":"d_test_proportion_diff(method)"},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/d_test_proportion_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Description of the difference test between two proportions — d_test_proportion_diff","text":"string describing test p-value derived.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of days to months — day2month","title":"Conversion of days to months — day2month","text":"Conversion days months","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of days to months — day2month","text":"","code":"day2month(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of days to months — day2month","text":"x (numeric(1)) time days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of days to months — day2month","text":"numeric vector time months.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/day2month.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of days to months — day2month","text":"","code":"x <- c(403, 248, 30, 86) day2month(x) #> [1] 13.2402464 8.1478439 0.9856263 2.8254620"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"function useful label grid grobs (also ggplot2, lattice plots) title, footnote, page numbers.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"","code":"decorate_grob( grob, titles, footnotes, page = \"\", width_titles = grid::unit(1, \"npc\"), width_footnotes = grid::unit(1, \"npc\"), border = TRUE, padding = grid::unit(rep(1, 4), \"lines\"), margins = grid::unit(c(1, 0, 1, 0), \"lines\"), outer_margins = grid::unit(c(2, 1.5, 3, 1.5), \"cm\"), gp_titles = grid::gpar(), gp_footnotes = grid::gpar(fontsize = 8), name = NULL, gp = grid::gpar(), vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"grob (grob) grid grob object, optionally NULL grob decoration shown. titles (character) titles given vector strings separated newline wrapped according page width. footnotes (character) footnotes. Uses formatting rules titles. page (string NULL) page numeration. NULL page number displayed. width_titles (grid::unit) width titles. Usually defined available space grid::unit(1, \"npc\"), affected parameter outer_margins. Right margins (outer_margins[4]) need subtracted allowed width. width_footnotes (grid::unit) width footnotes. default margin correction width_titles. border (flag) whether border drawn around plot . padding (grid::unit) padding. unit object length 4. Innermost margin plot (grob) , possibly, border plot. Usually expressed 4 identical values (usually \"lines\"). defaults grid::unit(rep(1, 4), \"lines\"). margins (grid::unit) margins. unit object length 4. Margins plot elements list (e.g. titles, plot, footers). usually expressed 4 \"lines\", lateral ones 0s, top bottom 1s. defaults grid::unit(c(1, 0, 1, 0), \"lines\"). outer_margins (grid::unit) outer margins. unit object length 4. defines general margin plot, considering also decorations like titles, footnotes, page numbers. defaults grid::unit(c(2, 1.5, 3, 1.5), \"cm\"). gp_titles (gpar) gpar object. Mainly used set different \"fontsize\". gp_footnotes (gpar) gpar object. Mainly used set different \"fontsize\". name character identifier grob. Used find grob display list /child another grob. gp \"gpar\" object, typically output call function gpar. basically list graphical parameter settings. vp viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"grid grob (gTree).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"titles footnotes ragged, .e. title wrapped individually.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add titles, footnotes, page Number, and a bounding box to a grid grob — decorate_grob","text":"","code":"library(grid) titles <- c( \"Edgar Anderson's Iris Data\", paste( \"This famous (Fisher's or Anderson's) iris data set gives the measurements\", \"in centimeters of the variables sepal length and width and petal length\", \"and width, respectively, for 50 flowers from each of 3 species of iris.\" ) ) footnotes <- c( \"The species are Iris setosa, versicolor, and virginica.\", paste( \"iris is a data frame with 150 cases (rows) and 5 variables (columns) named\", \"Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species.\" ) ) ## empty plot grid.newpage() grid.draw( decorate_grob( NULL, titles = titles, footnotes = footnotes, page = \"Page 4 of 10\" ) ) # grid p <- gTree( children = gList( rectGrob(), xaxisGrob(), yaxisGrob(), textGrob(\"Sepal.Length\", y = unit(-4, \"lines\")), textGrob(\"Petal.Length\", x = unit(-3.5, \"lines\"), rot = 90), pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16) ), vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length)) ) grid.newpage() grid.draw(p) grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) ## with ggplot2 library(ggplot2) p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) + ggplot2::geom_point() p_gg p <- ggplotGrob(p_gg) grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) ## with lattice library(lattice) xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species) p <- grid.grab() grid.newpage() grid.draw( decorate_grob( grob = p, titles = titles, footnotes = footnotes, page = \"Page 6 of 129\" ) ) # with gridExtra - no borders library(gridExtra) #> #> Attaching package: ‘gridExtra’ #> The following object is masked from ‘package:dplyr’: #> #> combine grid.newpage() grid.draw( decorate_grob( tableGrob( head(mtcars) ), titles = \"title\", footnotes = \"footnote\", border = FALSE ) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Update page number — decorate_grob_factory","title":"Update page number — decorate_grob_factory","text":"Automatically updates page number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update page number — decorate_grob_factory","text":"","code":"decorate_grob_factory(npages, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update page number — decorate_grob_factory","text":"npages (numeric(1)) total number pages. ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update page number — decorate_grob_factory","text":"Closure increments page number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Decorate set of grobs and add page numbering — decorate_grob_set","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"Note uses decorate_grob_factory() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"","code":"decorate_grob_set(grobs, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"grobs (list grob) list grid grobs. ... arguments passed decorate_grob().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"decorated grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/decorate_grob_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Decorate set of grobs and add page numbering — decorate_grob_set","text":"","code":"library(ggplot2) library(grid) g <- with(data = iris, { list( ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) + ggplot2::geom_point() ), ggplot2::ggplotGrob( ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) + ggplot2::geom_point() ) ) }) lg <- decorate_grob_set(grobs = g, titles = \"Hello\\nOne\\nTwo\\nThree\", footnotes = \"\") draw_grob(lg[[1]]) #> Warning: `draw_grob()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. draw_grob(lg[[2]]) draw_grob(lg[[6]])"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":null,"dir":"Reference","previous_headings":"","what":"Default string replacement for NA values — default_na_str","title":"Default string replacement for NA values — default_na_str","text":"default string used represent NA values. value used default value na_str argument throughout tern package, printed place NA values output tables. specified tern function user via na_str argument, R environment options via set_default_na_str(), NA used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default string replacement for NA values — default_na_str","text":"","code":"default_na_str() set_default_na_str(na_str)"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default string replacement for NA values — default_na_str","text":"na_str (string) single string value set R environment options default value replace NAs. Use getOption(\"tern_default_na_str\") check current value set R environment (defaults NULL set).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default string replacement for NA values — default_na_str","text":"default_na_str returns current value R environment option set \"tern_default_na_str\", NA_character_ otherwise. set_default_na_str return value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Default string replacement for NA values — default_na_str","text":"default_na_str(): Accessor default NA value replacement string. set_default_na_str(): Setter default NA value replacement string. Sets option \"tern_default_na_str\" within R environment.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_na_str.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default string replacement for NA values — default_na_str","text":"","code":"# Default settings default_na_str() #> [1] NA getOption(\"tern_default_na_str\") #> NULL # Set custom value set_default_na_str(\"\") # Settings after value has been set default_na_str() #> [1] \"\" getOption(\"tern_default_na_str\") #> [1] \"\""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"Utility functions get valid statistic methods different method groups (.stats) associated formats (.formats), labels (.labels), indent modifiers (.indent_mods). utility used across tern, working principles can seen analyze_vars(). See notes understand experimental.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"","code":"get_stats( method_groups = \"analyze_vars_numeric\", stats_in = NULL, add_pval = FALSE ) get_formats_from_stats(stats, formats_in = NULL) get_labels_from_stats(stats, labels_in = NULL, row_nms = NULL) get_indents_from_stats(stats, indents_in = NULL, row_nms = NULL) tern_default_stats tern_default_formats tern_default_labels summary_formats(type = \"numeric\", include_pval = FALSE) summary_labels(type = \"numeric\", include_pval = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"tern_default_stats named list available statistics, element named corresponding statistical method group. tern_default_formats named vector available default formats, element named corresponding statistic. tern_default_labels named character vector available default labels, element named corresponding statistic.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"method_groups (character) indicates statistical method group (tern analyze function) retrieve default statistics . character vector can used specify one statistical method group. stats_in (character) statistics retrieve selected method group. add_pval (flag) \"pval\" (\"pval_counts\" method_groups contains \"analyze_vars_counts\") added statistical methods? stats (character) statistical methods get defaults . formats_in (named vector) inserted formats replace defaults. can character vector formatters::list_valid_format_labels() custom format function. labels_in (named character) inserted labels replace defaults. row_nms (character) row names. Levels factor character variable, statistics .stats calculated . parameter set, variable levels used defaults, names given custom values correspond levels (format statistic.level) instead statistics. Can also variable names rows correspond different variables instead levels. Defaults NULL. indents_in (named vector) inserted indent modifiers replace defaults (default 0L). type (string)\"numeric\" \"counts\". include_pval (flag) add_pval argument get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"get_stats() returns character vector statistical methods. get_formats_from_stats() returns named vector formats (present either tern_default_formats formats_in, otherwise NULL). Values can taken formatters::list_valid_format_labels() custom function (e.g. formatting_functions). get_labels_from_stats() returns named character vector labels (present either tern_default_labels labels_in, otherwise NULL). get_indents_from_stats() returns single indent modifier value apply rows named numeric vector indent modifiers (present, otherwise NULL). summary_formats() returns named vector default statistic formats given data type. summary_labels returns named vector default statistic labels given data type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"Current choices type counts numeric analyze_vars() affect get_stats().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"get_stats(): Get statistics available given method group (analyze function). check available defaults see tern::tern_default_stats list. get_formats_from_stats(): Get formats corresponding list statistics. check available defaults see tern::tern_default_formats list. get_labels_from_stats(): Get labels corresponding list statistics. check available defaults see tern::tern_default_labels list. available , statistics name used label. get_indents_from_stats(): Format indent modifiers given vector/list statistics. defaults 0L values. tern_default_stats: Named list available statistics method group tern. tern_default_formats: Named vector default formats tern. tern_default_labels: Named character vector default labels tern. summary_formats(): Quick function retrieve default formats summary statistics: analyze_vars() analyze_vars_in_cols() principally. summary_labels(): Quick function retrieve default labels summary statistics. Returns labels descriptive statistics understood rtables. Similar summary_formats.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"defaults experimental use names functions retrieve default statistics. generalized groups methods according reasonable groupings. Formats tern rtables can functions take table cell value return string. well documented vignette(\"custom_appearance\", package = \"rtables\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/default_stats_formats_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get default statistical methods and their associated formats, labels, and indent modifiers — default_stats_formats_labels","text":"","code":"# analyze_vars is numeric num_stats <- get_stats(\"analyze_vars_numeric\") # also the default # Other type cnt_stats <- get_stats(\"analyze_vars_counts\") # Weirdly taking the pval from count_occurrences only_pval <- get_stats(\"count_occurrences\", add_pval = TRUE, stats_in = \"pval\") # All count_occurrences all_cnt_occ <- get_stats(\"count_occurrences\") # Multiple get_stats(c(\"count_occurrences\", \"analyze_vars_counts\")) #> [1] \"count\" \"count_fraction\" #> [3] \"count_fraction_fixed_dp\" \"fraction\" #> [5] \"n\" \"n_blq\" # Defaults formats get_formats_from_stats(num_stats) #> $n #> [1] \"xx.\" #> #> $sum #> [1] \"xx.x\" #> #> $mean #> [1] \"xx.x\" #> #> $sd #> [1] \"xx.x\" #> #> $se #> [1] \"xx.x\" #> #> $mean_sd #> [1] \"xx.x (xx.x)\" #> #> $mean_se #> [1] \"xx.x (xx.x)\" #> #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_pval #> [1] \"x.xxxx | (<0.0001)\" #> #> $median #> [1] \"xx.x\" #> #> $mad #> [1] \"xx.x\" #> #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $quantiles #> [1] \"xx.x - xx.x\" #> #> $iqr #> [1] \"xx.x\" #> #> $range #> [1] \"xx.x - xx.x\" #> #> $min #> [1] \"xx.x\" #> #> $max #> [1] \"xx.x\" #> #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #> #> $cv #> [1] \"xx.x\" #> #> $geom_mean #> [1] \"xx.x\" #> #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $geom_cv #> [1] \"xx.x\" #> get_formats_from_stats(cnt_stats) #> $n #> [1] \"xx.\" #> #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> #> $n_blq #> [1] \"xx.\" #> get_formats_from_stats(only_pval) #> $pval #> [1] \"x.xxxx | (<0.0001)\" #> get_formats_from_stats(all_cnt_occ) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> # Addition of customs get_formats_from_stats(all_cnt_occ, formats_in = c(\"fraction\" = c(\"xx\"))) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> [1] \"xx\" #> get_formats_from_stats(all_cnt_occ, formats_in = list(\"fraction\" = c(\"xx.xx\", \"xx\"))) #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> [1] \"xx.xx\" \"xx\" #> # Defaults labels get_labels_from_stats(num_stats) #> n sum #> \"n\" \"Sum\" #> mean sd #> \"Mean\" \"SD\" #> se mean_sd #> \"SE\" \"Mean (SD)\" #> mean_se mean_ci #> \"Mean (SE)\" \"Mean 95% CI\" #> mean_sei mean_sdi #> \"Mean -/+ 1xSE\" \"Mean -/+ 1xSD\" #> mean_pval median #> \"Mean p-value (H0: mean = 0)\" \"Median\" #> mad median_ci #> \"Median Absolute Deviation\" \"Median 95% CI\" #> quantiles iqr #> \"25% and 75%-ile\" \"IQR\" #> range min #> \"Min - Max\" \"Minimum\" #> max median_range #> \"Maximum\" \"Median (Min - Max)\" #> cv geom_mean #> \"CV (%)\" \"Geometric Mean\" #> geom_mean_ci geom_cv #> \"Geometric Mean 95% CI\" \"CV % Geometric Mean\" get_labels_from_stats(cnt_stats) #> n count count_fraction #> \"n\" \"count\" \"count_fraction\" #> count_fraction_fixed_dp fraction n_blq #> \"count_fraction\" \"fraction\" \"n_blq\" get_labels_from_stats(only_pval) #> pval #> \"p-value (t-test)\" get_labels_from_stats(all_cnt_occ) #> count count_fraction count_fraction_fixed_dp #> \"count\" \"count_fraction\" \"count_fraction\" #> fraction #> \"fraction\" # Addition of customs get_labels_from_stats(all_cnt_occ, labels_in = c(\"fraction\" = \"Fraction\")) #> count count_fraction count_fraction_fixed_dp #> \"count\" \"count_fraction\" \"count_fraction\" #> fraction #> \"Fraction\" get_labels_from_stats(all_cnt_occ, labels_in = list(\"fraction\" = c(\"Some more fractions\"))) #> $count #> [1] \"count\" #> #> $count_fraction #> [1] \"count_fraction\" #> #> $count_fraction_fixed_dp #> [1] \"count_fraction\" #> #> $fraction #> [1] \"Some more fractions\" #> get_indents_from_stats(all_cnt_occ, indents_in = 3L) #> [1] 3 3 3 3 get_indents_from_stats(all_cnt_occ, indents_in = list(count = 2L, count_fraction = 5L)) #> $count #> [1] 2 #> #> $count_fraction #> [1] 5 #> #> $count_fraction_fixed_dp #> [1] 0 #> #> $fraction #> [1] 0 #> get_indents_from_stats( all_cnt_occ, indents_in = list(a = 2L, count.a = 1L, count.b = 5L), row_nms = c(\"a\", \"b\") ) #> $count.a #> [1] 1 #> #> $count.b #> [1] 5 #> #> $count_fraction.a #> [1] 2 #> #> $count_fraction.b #> [1] 0 #> #> $count_fraction_fixed_dp.a #> [1] 2 #> #> $count_fraction_fixed_dp.b #> [1] 0 #> #> $fraction.a #> [1] 2 #> #> $fraction.b #> [1] 0 #> summary_formats() #> Warning: `summary_formats()` was deprecated in tern 0.9.6. #> ℹ Use get_formats_from_stats(get_stats(\"analyze_vars_numeric\", add_pval = #> include_pval)) instead #> $n #> [1] \"xx.\" #> #> $sum #> [1] \"xx.x\" #> #> $mean #> [1] \"xx.x\" #> #> $sd #> [1] \"xx.x\" #> #> $se #> [1] \"xx.x\" #> #> $mean_sd #> [1] \"xx.x (xx.x)\" #> #> $mean_se #> [1] \"xx.x (xx.x)\" #> #> $mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sei #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_sdi #> [1] \"(xx.xx, xx.xx)\" #> #> $mean_pval #> [1] \"x.xxxx | (<0.0001)\" #> #> $median #> [1] \"xx.x\" #> #> $mad #> [1] \"xx.x\" #> #> $median_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $quantiles #> [1] \"xx.x - xx.x\" #> #> $iqr #> [1] \"xx.x\" #> #> $range #> [1] \"xx.x - xx.x\" #> #> $min #> [1] \"xx.x\" #> #> $max #> [1] \"xx.x\" #> #> $median_range #> [1] \"xx.x (xx.x - xx.x)\" #> #> $cv #> [1] \"xx.x\" #> #> $geom_mean #> [1] \"xx.x\" #> #> $geom_mean_ci #> [1] \"(xx.xx, xx.xx)\" #> #> $geom_cv #> [1] \"xx.x\" #> summary_formats(type = \"counts\", include_pval = TRUE) #> $n #> [1] \"xx.\" #> #> $count #> [1] \"xx.\" #> #> $count_fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else { #> paste0(x[1], \" (\", round(x[2] * 100, 1), \"%)\") #> } #> #> return(result) #> } #> #> #> $count_fraction_fixed_dp #> function(x, ...) { #> attr(x, \"label\") <- NULL #> #> if (any(is.na(x))) { #> return(\"NA\") #> } #> #> checkmate::assert_vector(x) #> checkmate::assert_integerish(x[1]) #> assert_proportion_value(x[2], include_boundaries = TRUE) #> #> result <- if (x[1] == 0) { #> \"0\" #> } else if (.is_equal_float(x[2], 1)) { #> sprintf(\"%d (100%%)\", x[1]) #> } else { #> sprintf(\"%d (%.1f%%)\", x[1], x[2] * 100) #> } #> #> return(result) #> } #> #> #> $fraction #> function(x, ...) { #> attr(x, \"label\") <- NULL #> checkmate::assert_vector(x) #> checkmate::assert_count(x[\"num\"]) #> checkmate::assert_count(x[\"denom\"]) #> #> result <- if (x[\"num\"] == 0) { #> paste0(x[\"num\"], \"/\", x[\"denom\"]) #> } else { #> paste0( #> x[\"num\"], \"/\", x[\"denom\"], #> \" (\", sprintf(\"%.1f\", round(x[\"num\"] / x[\"denom\"] * 100, 1)), \"%)\" #> ) #> } #> return(result) #> } #> #> #> $n_blq #> [1] \"xx.\" #> #> $pval_counts #> [1] \"x.xxxx | (<0.0001)\" #> summary_labels() #> Warning: `summary_formats()` was deprecated in tern 0.9.6. #> ℹ Use get_labels_from_stats(get_stats(\"analyze_vars_numeric\", add_pval = #> include_pval)) instead #> n sum #> \"n\" \"Sum\" #> mean sd #> \"Mean\" \"SD\" #> se mean_sd #> \"SE\" \"Mean (SD)\" #> mean_se mean_ci #> \"Mean (SE)\" \"Mean 95% CI\" #> mean_sei mean_sdi #> \"Mean -/+ 1xSE\" \"Mean -/+ 1xSD\" #> mean_pval median #> \"Mean p-value (H0: mean = 0)\" \"Median\" #> mad median_ci #> \"Median Absolute Deviation\" \"Median 95% CI\" #> quantiles iqr #> \"25% and 75%-ile\" \"IQR\" #> range min #> \"Min - Max\" \"Minimum\" #> max median_range #> \"Maximum\" \"Median (Min - Max)\" #> cv geom_mean #> \"CV (%)\" \"Geometric Mean\" #> geom_mean_ci geom_cv #> \"Geometric Mean 95% CI\" \"CV % Geometric Mean\" summary_labels(type = \"counts\", include_pval = TRUE) #> n count #> \"n\" \"count\" #> count_fraction count_fraction_fixed_dp #> \"count_fraction\" \"count_fraction\" #> fraction n_blq #> \"fraction\" \"n_blq\" #> pval_counts #> \"p-value (chi-squared test)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for a difference of binomials — desctools_binom","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"Several confidence intervals difference proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"","code":"desctools_binom( x1, n1, x2, n2, conf.level = 0.95, sides = c(\"two.sided\", \"left\", \"right\"), method = c(\"ac\", \"wald\", \"waldcc\", \"score\", \"scorecc\", \"mn\", \"mee\", \"blj\", \"ha\", \"hal\", \"jp\") ) desctools_binomci( x, n, conf.level = 0.95, sides = c(\"two.sided\", \"left\", \"right\"), method = c(\"wilson\", \"wald\", \"waldcc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"wilsoncc\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\"), rand = 123, tol = 1e-05 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"conf.level (proportion) confidence level, defaults 0.95. sides (string) side confidence interval compute. Must one \"two-sided\" (default), \"left\", \"right\". method (string) method use. Can one : \"wald\", \"wilson\", \"wilsoncc\", \"agresti-coull\", \"jeffreys\", \"modified wilson\", \"modified jeffreys\", \"clopper-pearson\", \"arcsine\", \"logit\", \"witting\", \"pratt\", \"midp\", \"lik\", \"blaker\". x (integer(1)) number successes. n (integer(1)) number trials.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"matrix 3 values: est: estimate proportion difference. lwr.ci: estimate lower end confidence interval. upr.ci: estimate upper end confidence interval. matrix 3 columns containing: est: estimate proportion difference. lwr.ci: lower end confidence interval. upr.ci: upper end confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/desctools_binom.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Confidence intervals for a difference of binomials — desctools_binom","text":"desctools_binom(): Several confidence intervals difference proportions. desctools_binomci(): Compute confidence intervals binomial proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data.frame object to ggplot object — df2gg","title":"Convert data.frame object to ggplot object — df2gg","text":"Given data.frame object, performs basic conversion ggplot2::ggplot() object built using functions ggplot2 package.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data.frame object to ggplot object — df2gg","text":"","code":"df2gg( df, colwidths = NULL, font_size = 10, col_labels = TRUE, col_lab_fontface = \"bold\", hline = TRUE, bg_fill = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data.frame object to ggplot object — df2gg","text":"df (data.frame) data frame. colwidths (numeric NULL) vector column widths. element's position colwidths corresponds column df position. NULL, column widths calculated according maximum number characters per column. font_size (numeric(1)) font size. col_labels (flag) whether column names (labels) df used first row output table. col_lab_fontface (string) font face apply first row (column labels col_labels = TRUE). Defaults \"bold\". hline (flag) whether horizontal line printed first row table. bg_fill (string) table background fill color.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data.frame object to ggplot object — df2gg","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df2gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data.frame object to ggplot object — df2gg","text":"","code":"if (FALSE) { # \\dontrun{ df2gg(head(iris, 5)) df2gg(head(iris, 5), font_size = 15, colwidths = c(1, 1, 1, 1, 1)) } # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Encode categorical missing values in a data frame — df_explicit_na","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"helper function encode missing entries across groups categorical variables data frame.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"","code":"df_explicit_na( data, omit_columns = NULL, char_as_factor = TRUE, logical_as_factor = FALSE, na_level = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"data (data.frame) data set. omit_columns (character) names variables data modified function. char_as_factor (flag) whether convert character variables data factors. logical_as_factor (flag) whether convert logical variables data factors. na_level (string) string used replace NA empty values inside non-omit_columns columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"data.frame chosen modifications applied.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"Missing entries NA empty strings replaced specified value. factor variables include missing values, missing value inserted last level. Similarly, case character logical variables converted factors char_as_factor logical_as_factor options, missing values set last level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/df_explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Encode categorical missing values in a data frame — df_explicit_na","text":"","code":"my_data <- data.frame( u = c(TRUE, FALSE, NA, TRUE), v = factor(c(\"A\", NA, NA, NA), levels = c(\"Z\", \"A\")), w = c(\"A\", \"B\", NA, \"C\"), x = c(\"D\", \"E\", \"F\", NA), y = c(\"G\", \"H\", \"I\", \"\"), z = c(1, 2, 3, 4), stringsAsFactors = FALSE ) # Example 1 # Encode missing values in all character or factor columns. df_explicit_na(my_data) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 NA F I 3 #> 4 TRUE C 4 # Also convert logical columns to factor columns. df_explicit_na(my_data, logical_as_factor = TRUE) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 F I 3 #> 4 TRUE C 4 # Encode missing values in a subset of columns. df_explicit_na(my_data, omit_columns = c(\"x\", \"y\")) #> u v w x y z #> 1 TRUE A A D G 1 #> 2 FALSE B E H 2 #> 3 NA F I 3 #> 4 TRUE C 4 # Example 2 # Here we purposefully convert all `M` values to `NA` in the `SEX` variable. # After running `df_explicit_na` the `NA` values are encoded as `` but they are not # included when generating `rtables`. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl) # If you want the `Na` values to be displayed in the table use the `na_level` argument. adsl <- tern_ex_adsl adsl$SEX[adsl$SEX == \"M\"] <- NA adsl <- df_explicit_na(adsl, na_level = \"Missing Values\") # Example 3 # Numeric variables that have missing values are not altered. This means that any `NA` value in # a numeric variable will not be included in the summary statistics, nor will they be included # in the denominator value for calculating the percent values. adsl <- tern_ex_adsl adsl$AGE[adsl$AGE < 30] <- NA adsl <- df_explicit_na(adsl)"},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to check if a float value is equal to another float value — .is_equal_float","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"Uses .Machine$double.eps tolerance comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"","code":".is_equal_float(x, y)"},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"x (numeric(1)) float number. y (numeric(1)) float number.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/dot-is_equal_float.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to check if a float value is equal to another float value — .is_equal_float","text":"TRUE identical, otherwise FALSE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Draw grob — draw_grob","title":"Draw grob — draw_grob","text":"Draw grob device page.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Draw grob — draw_grob","text":"","code":"draw_grob(grob, newpage = TRUE, vp = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Draw grob — draw_grob","text":"grob (grob) grid object. newpage (flag) draw new page. vp (viewport NULL) viewport() object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Draw grob — draw_grob","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/draw_grob.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Draw grob — draw_grob","text":"","code":"library(dplyr) library(grid) # \\donttest{ rect <- rectGrob(width = grid::unit(0.5, \"npc\"), height = grid::unit(0.5, \"npc\")) rect %>% draw_grob(vp = grid::viewport(angle = 45)) num <- lapply(1:10, textGrob) num %>% arrange_grobs(grobs = .) %>% draw_grob() #> Warning: `stack_grobs()` was deprecated in tern 0.9.4. #> ℹ `tern` plotting functions no longer generate `grob` objects. #> ℹ The deprecated feature was likely used in the tern package. #> Please report the issue at #> . showViewport() # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Return an empty numeric if all elements are NA. — empty_vector_if_na","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"Return empty numeric elements NA.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"empty_vector_if_na(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"x (numeric) vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"empty numeric elements x NA, otherwise x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/empty_vector_if_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return an empty numeric if all elements are NA. — empty_vector_if_na","text":"","code":"x <- c(NA, NA, NA) # Internal function - empty_vector_if_na"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Hazard ratio estimation in interactions — estimate_coef","title":"Hazard ratio estimation in interactions — estimate_coef","text":"function estimates hazard ratios arms interaction variable given specific values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hazard ratio estimation in interactions — estimate_coef","text":"","code":"estimate_coef( variable, given, lvl_var, lvl_given, coef, mmat, vcov, conf_level = 0.95 )"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hazard ratio estimation in interactions — estimate_coef","text":"variable, given (character(2)) names two variables interaction. seek estimation levels variable given levels given. lvl_var, lvl_given (character) corresponding levels given levels(). coef (numeric) vector estimated coefficients. mmat (named numeric) vector filled 0s used template obtain design matrix. vcov (matrix) variance-covariance matrix underlying model. conf_level (proportion) confidence level estimate intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hazard ratio estimation in interactions — estimate_coef","text":"list matrices (one per level variable) rows corresponding combinations variable given, columns: coef_hat: Estimation coefficient. coef_se: Standard error estimation. hr: Hazard ratio. lcl, ucl: Lower/upper confidence limit hazard ratio.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hazard ratio estimation in interactions — estimate_coef","text":"Given cox regression investigating effect Arm (, B, C; reference ) Sex (F, M; reference Female). model abbreviated: y ~ Arm + Sex + Arm x Sex. cox regression estimates coefficients along variance-covariance matrix : b1 (arm b), b2 (arm c) b3 (sex m) b4 (arm b: sex m), b5 (arm c: sex m) Given want estimation Hazard Ratio arm C/sex M, estimation given reference arm /Sex M exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5), therefore interaction coefficient given b2 + b5 standard error obtained $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ confidence level 0.95.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hazard ratio estimation in interactions — estimate_coef","text":"","code":"library(dplyr) library(survival) ADSL <- tern_ex_adsl %>% filter(SEX %in% c(\"F\", \"M\")) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"PFS\") adtte$ARMCD <- droplevels(adtte$ARMCD) adtte$SEX <- droplevels(adtte$SEX) mod <- coxph( formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2, data = adtte ) mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"analyze & summarize function estimate_multinomial_response() creates layout element estimate proportion proportion confidence interval level factor variable. primary analysis variable, var, factor variable, values used labels within output table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"","code":"estimate_multinomial_response( lyt, var, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = var, .stats = \"prop_ci\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_length_proportion(x, .N_col, ...) a_length_proportion(x, .N_col, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_multinomial_response\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. x (numeric) vector numbers want analyze. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"estimate_multinomial_response() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_length_proportion() table layout. s_length_proportion() returns statistics s_proportion(). a_length_proportion() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"estimate_multinomial_response(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze() rtables::summarize_row_groups(). s_length_proportion(): Statistics function feeds length x number successes, .N_col total number successes failures s_proportion(). a_length_proportion(): Formatted analysis function used afun estimate_multinomial_response().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_multinomial_rsp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate proportions of each level of a variable — estimate_multinomial_rsp","text":"","code":"library(dplyr) # Use of the layout creating function. dta_test <- data.frame( USUBJID = paste0(\"S\", 1:12), ARM = factor(rep(LETTERS[1:3], each = 4)), AVAL = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0)) ) %>% mutate( AVALC = factor(AVAL, levels = c(0, 1), labels = c(\"Complete Response (CR)\", \"Partial Response (PR)\") ) ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% estimate_multinomial_response(var = \"AVALC\") tbl <- build_table(lyt, dta_test) tbl #> A B C #> ————————————————————————————————————————————————————————————————————————————————————— #> Complete Response (CR) 0 (0.0%) 2 (50.0%) 4 (100.0%) #> 95% CI (Wald, with correction) (0.00, 12.50) (0.00, 100.00) (87.50, 100.00) #> Partial Response (PR) 4 (100.0%) 2 (50.0%) 0 (0.0%) #> 95% CI (Wald, with correction) (87.50, 100.00) (0.00, 100.00) (0.00, 12.50) s_length_proportion(rep(\"CR\", 10), .N_col = 100) #> $n_prop #> [1] 10.0 0.1 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 3.620108 16.379892 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> s_length_proportion(factor(character(0)), .N_col = 100) #> $n_prop #> [1] 0 0 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 0.0 0.5 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> a_length_proportion(rep(\"CR\", 10), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n_prop 10 (10.0%) 0 Responders #> 2 prop_ci (3.62, 16.38) 0 95% CI (Wald, with correction) a_length_proportion(factor(character(0)), .N_col = 100) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n_prop 0 (0.0%) 0 Responders #> 2 prop_ci (0.00, 0.50) 0 95% CI (Wald, with correction)"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion estimation — estimate_proportion","title":"Proportion estimation — estimate_proportion","text":"analyze function estimate_proportion() creates layout element estimate proportion responders within studied population. primary analysis variable, vars, indicates whether response occurred record. See method parameter options methods use constructing confidence interval proportion. Additionally, stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion estimation — estimate_proportion","text":"","code":"estimate_proportion( lyt, vars, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_proportion( df, .var, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE ) a_proportion( df, .var, conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"clopper-pearson\", \"wilson\", \"wilsonc\", \"strat_wilson\", \"strat_wilsonc\", \"agresti-coull\", \"jeffreys\"), weights = NULL, max_iterations = 50, variables = list(strata = NULL), long = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion estimation — estimate_proportion","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . conf_level (proportion) confidence level interval. method (string) method used construct confidence interval proportion successful outcomes; one waldcc, wald, clopper-pearson, wilson, wilsonc, strat_wilson, strat_wilsonc, agresti-coull jeffreys. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights. variables (named list string) list additional analysis variables. long (flag) whether long description required. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_proportion\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (logical data.frame) logical vector used, indicates whether subject responder . TRUE represents successful outcome. data.frame provided, also strata variable names must provided variables list element strata strings. case data.frame, logical vector responses must indicated variable name .var. .var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion estimation — estimate_proportion","text":"estimate_proportion() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion() table layout. s_proportion() returns statistics n_prop (n proportion) prop_ci (proportion CI) given variable. a_proportion() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion estimation — estimate_proportion","text":"estimate_proportion(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_proportion(): Statistics function estimating proportion along confidence interval. a_proportion(): Formatted analysis function used afun estimate_proportion().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/estimate_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion estimation — estimate_proportion","text":"","code":"dta_test <- data.frame( USUBJID = paste0(\"S\", 1:12), ARM = rep(LETTERS[1:3], each = 4), AVAL = rep(LETTERS[1:3], each = 4) ) basic_table() %>% split_cols_by(\"ARM\") %>% estimate_proportion(vars = \"AVAL\") %>% build_table(df = dta_test) #> A B C #> —————————————————————————————————————————————————————————————————— #> Responders #> 95% CI (Wald, with correction) # Case with only logical vector. rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0) s_proportion(rsp_v) #> $n_prop #> [1] 4.0 0.5 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> [1] 9.102404 90.897596 #> attr(,\"label\") #> [1] \"95% CI (Wald, with correction)\" #> # Example for Stratified Wilson CI nex <- 100 # Number of example rows dta <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE), \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE), \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE), stringsAsFactors = TRUE ) s_proportion( df = dta, .var = \"rsp\", variables = list(strata = c(\"f1\", \"f2\")), conf_level = 0.90, method = \"strat_wilson\" ) #> $n_prop #> [1] 49.00 0.49 #> attr(,\"label\") #> [1] \"Responders\" #> #> $prop_ci #> lower upper #> 40.80675 56.65017 #> attr(,\"label\") #> [1] \"90% CI (Stratified Wilson, without correction)\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulated CDISC data for examples — ex_data","title":"Simulated CDISC data for examples — ex_data","text":"Simulated CDISC data examples","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulated CDISC data for examples — ex_data","text":"","code":"tern_ex_adsl tern_ex_adae tern_ex_adlb tern_ex_adpp tern_ex_adrs tern_ex_adtte"},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Simulated CDISC data for examples — ex_data","text":"rds (data.frame) object class tbl_df (inherits tbl, data.frame) 200 rows 21 columns. object class tbl_df (inherits tbl, data.frame) 541 rows 42 columns. object class tbl_df (inherits tbl, data.frame) 4200 rows 50 columns. object class tbl_df (inherits tbl, data.frame) 522 rows 25 columns. object class tbl_df (inherits tbl, data.frame) 1600 rows 29 columns. object class tbl_df (inherits tbl, data.frame) 1000 rows 28 columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/ex_data.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Simulated CDISC data for examples — ex_data","text":"tern_ex_adsl: ADSL data tern_ex_adae: ADAE data tern_ex_adlb: ADLB data tern_ex_adpp: ADPP data tern_ex_adrs: ADRS data tern_ex_adtte: ADTTE data","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing data — explicit_na","title":"Missing data — explicit_na","text":"Substitute missing data string factor level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing data — explicit_na","text":"","code":"explicit_na(x, label = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing data — explicit_na","text":"x (factor character) values missing values substituted. label (string) string missing data replaced .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing data — explicit_na","text":"x NA values substituted label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/explicit_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing data — explicit_na","text":"","code":"explicit_na(c(NA, \"a\", \"b\")) #> [1] \"\" \"a\" \"b\" is.na(explicit_na(c(NA, \"a\", \"b\"))) #> [1] FALSE FALSE FALSE explicit_na(factor(c(NA, \"a\", \"b\"))) #> [1] a b #> Levels: a b is.na(explicit_na(factor(c(NA, \"a\", \"b\")))) #> [1] FALSE FALSE FALSE explicit_na(sas_na(c(\"a\", \"\"))) #> [1] \"a\" \"\""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract elements by name — extract_by_name","title":"Extract elements by name — extract_by_name","text":"utility function extracts elements vector x names. Differences standard [ function :","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract elements by name — extract_by_name","text":"","code":"extract_by_name(x, names)"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract elements by name — extract_by_name","text":"x (named vector) extract named elements . names (character) vector names extract.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract elements by name — extract_by_name","text":"NULL x NULL, otherwise extracted elements x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_by_name.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract elements by name — extract_by_name","text":"x NULL, still always NULL returned (base function). x NULL, intersection names made names elements returned. , names appear x returned NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"Prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates, subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"","code":"extract_rsp_biomarkers( variables, data, groups_lists = list(), control = control_logistic(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (named list) controls response definition confidence level produced control_logistic(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_rsp, prop, , lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"can also specify continuous variable rsp use response_definition control convert internally logical variable reflecting binary response.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_biomarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare response data estimates for multiple biomarkers in a single data frame — extract_rsp_biomarkers","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`, # in logistic regression models with one covariate `RACE`. The subgroups # are defined by the levels of `BMRKR2`. df <- extract_rsp_biomarkers( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\" ), data = adrs_f ) df #> biomarker biomarker_label n_tot n_rsp prop or #> 1 BMRKR1 Continuous Level Biomarker 1 200 164 0.8200000 0.9755036 #> 2 AGE Age 200 164 0.8200000 0.9952416 #> 3 BMRKR1 Continuous Level Biomarker 1 70 53 0.7571429 1.1524547 #> 4 AGE Age 70 53 0.7571429 0.9261012 #> 5 BMRKR1 Continuous Level Biomarker 1 68 58 0.8529412 0.8773122 #> 6 AGE Age 68 58 0.8529412 0.9867104 #> 7 BMRKR1 Continuous Level Biomarker 1 62 53 0.8548387 0.8792921 #> 8 AGE Age 62 53 0.8548387 1.0630262 #> lcl ucl conf_level pval pval_label subgroup var #> 1 0.8804862 1.080775 0.95 0.6352602 p-value (Wald) All Patients ALL #> 2 0.9462617 1.046757 0.95 0.8530389 p-value (Wald) All Patients ALL #> 3 0.9462127 1.403650 0.95 0.1584187 p-value (Wald) LOW BMRKR2 #> 4 0.8487519 1.010500 0.95 0.0844837 p-value (Wald) LOW BMRKR2 #> 5 0.7277189 1.057657 0.95 0.1699778 p-value (Wald) MEDIUM BMRKR2 #> 6 0.8798911 1.106498 0.95 0.8189816 p-value (Wald) MEDIUM BMRKR2 #> 7 0.7189748 1.075357 0.95 0.2103709 p-value (Wald) HIGH BMRKR2 #> 8 0.9595973 1.177603 0.95 0.2418840 p-value (Wald) HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Continuous Level Biomarker 2 analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis # Here we group the levels of `BMRKR2` manually, and we add a stratification # variable `STRATA1`. We also here use a continuous variable `EOSDY` # which is then binarized internally (response is defined as this variable # being larger than 750). df_grouped <- extract_rsp_biomarkers( variables = list( rsp = \"EOSDY\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\", strata = \"STRATA1\" ), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ), control = control_logistic( response_definition = \"I(response > 750)\" ) ) df_grouped #> biomarker biomarker_label n_tot n_rsp prop or lcl ucl conf_level #> 1 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95 #> 2 AGE Age 200 0 0 NA NA NA 0.95 #> 3 BMRKR1 Continuous Level Biomarker 1 70 0 0 NA NA NA 0.95 #> 4 AGE Age 70 0 0 NA NA NA 0.95 #> 5 BMRKR1 Continuous Level Biomarker 1 138 0 0 NA NA NA 0.95 #> 6 AGE Age 138 0 0 NA NA NA 0.95 #> 7 BMRKR1 Continuous Level Biomarker 1 200 0 0 NA NA NA 0.95 #> 8 AGE Age 200 0 0 NA NA NA 0.95 #> pval pval_label subgroup var var_label #> 1 NA p-value (Wald) All Patients ALL All Patients #> 2 NA p-value (Wald) All Patients ALL All Patients #> 3 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2 #> 4 NA p-value (Wald) low BMRKR2 Continuous Level Biomarker 2 #> 5 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 NA p-value (Wald) low/medium BMRKR2 Continuous Level Biomarker 2 #> 7 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> 8 NA p-value (Wald) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> row_type #> 1 content #> 2 content #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis #> 7 analysis #> 8 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"Prepares response rates odds ratios population subgroups data frames. Simple wrapper h_odds_ratio_subgroups_df() h_proportion_subgroups_df(). Result list two data.frames: prop . variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"","code":"extract_rsp_subgroups( variables, data, groups_lists = list(), conf_level = 0.95, method = NULL, label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_rsp_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare response data for population subgroups in data frames — extract_rsp_subgroups","text":"named list two elements: prop: data.frame containing columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. : data.frame containing columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers across population subgroups single data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables), optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"","code":"extract_survival_biomarkers( variables, data, groups_lists = list(), control = control_coxreg(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) list parameters returned helper function control_coxreg(). label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_biomarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare survival data estimates for multiple biomarkers in a single data frame — extract_survival_biomarkers","text":"data.frame columns biomarker, biomarker_label, n_tot, n_tot_events, median, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"Prepares estimates median survival times treatment hazard ratios population subgroups data frames. Simple wrapper h_survtime_subgroups_df() h_coxph_subgroups_df(). Result list two data.frames: survtime hr. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"","code":"extract_survival_subgroups( variables, data, groups_lists = list(), control = control_coxph(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing null hypothesis hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval HR. label_all (string) label total population analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extract_survival_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare survival data for population subgroups in data frames — extract_survival_subgroups","text":"named list two elements: survtime: data.frame containing columns arm, n, n_events, median, subgroup, var, var_label, row_type. hr: data.frame containing columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Format extreme values — extreme_format","title":"Format extreme values — extreme_format","text":"rtables formatting functions handle extreme values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format extreme values — extreme_format","text":"","code":"h_get_format_threshold(digits = 2L) h_format_threshold(x, digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format extreme values — extreme_format","text":"digits (integer(1)) number decimal places display. x (numeric(1)) value format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format extreme values — extreme_format","text":"h_get_format_threshold() returns list 2 elements: threshold, low high thresholds, format_string, thresholds formatted strings. h_format_threshold() returns given value, value within digit threshold relation given value digit threshold, formatted string.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Format extreme values — extreme_format","text":"input, apply format specified number digits. value threshold, returns \"<0.01\" e.g. number digits 2. value threshold, returns \">999.99\" e.g. number digits 2. zero, returns \"0.00\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Format extreme values — extreme_format","text":"h_get_format_threshold(): Internal helper function calculate threshold create formatted strings used Formatting Functions. Returns list elements threshold format_string. h_format_threshold(): Internal helper function apply threshold format value. Creates formatted string used Formatting Functions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/extreme_format.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format extreme values — extreme_format","text":"","code":"h_get_format_threshold(2L) #> $threshold #> low high #> 0.01 999.99 #> #> $format_string #> low high #> \"<0.01\" \">999.99\" #> h_format_threshold(0.001) #> [1] \"<0.01\" h_format_threshold(1000) #> [1] \">999.99\""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for confidence interval — f_conf_level","text":"","code":"f_conf_level(conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for confidence interval — f_conf_level","text":"conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/f_conf_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for confidence interval — f_conf_level","text":"string.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to create label for p-value — f_pval","text":"","code":"f_pval(test_mean)"},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to create label for p-value — f_pval","text":"test_mean (numeric(1)) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/f_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to create label for p-value — f_pval","text":"string.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels and keep only those new group levels — fct_collapse_only","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"collapses levels keeps new group levels, order provided. returned factor levels order given, possible missing level last (included missing values).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"","code":"fct_collapse_only(.f, ..., .na_level = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":".f (factor character) original vector. ... (named character) levels vector provided collapsed new level given respective name. .na_level (string) level use levels, missing new factor. Note level must contained new levels specified ....","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"modified factor collapsed levels. Values levels included given character vector input set missing level .na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"existing NAs input vector replaced missing level. needed, explicit_na() can called separately result.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_collapse_only.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels and keep only those new group levels — fct_collapse_only","text":"","code":"fct_collapse_only(factor(c(\"a\", \"b\", \"c\", \"d\")), TRT = \"b\", CTRL = c(\"c\", \"d\")) #> [1] TRT CTRL CTRL #> Levels: TRT CTRL "},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":null,"dir":"Reference","previous_headings":"","what":"Discard specified levels of a factor — fct_discard","title":"Discard specified levels of a factor — fct_discard","text":"discards observations well levels specified factor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discard specified levels of a factor — fct_discard","text":"","code":"fct_discard(x, discard)"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discard specified levels of a factor — fct_discard","text":"x (factor) original factor. discard (character) levels discard.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discard specified levels of a factor — fct_discard","text":"modified factor observations well levels discard dropped.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_discard.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discard specified levels of a factor — fct_discard","text":"","code":"fct_discard(factor(c(\"a\", \"b\", \"c\")), \"c\") #> [1] a b #> Levels: a b"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":null,"dir":"Reference","previous_headings":"","what":"Insertion of explicit missing values in a factor — fct_explicit_na_if","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"inserts explicit missing values factor based condition. Additionally, existing NA values explicitly converted given na_level.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(x, condition, na_level = \"\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"x (factor) original factor. condition (logical) positions insert missing values. na_level (string) level use missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"modified factor inserted existing NA converted na_level.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fct_explicit_na_if.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insertion of explicit missing values in a factor — fct_explicit_na_if","text":"","code":"fct_explicit_na_if(factor(c(\"a\", \"b\", NA)), c(TRUE, FALSE, FALSE)) #> [1] b #> Levels: a b "},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Fitting functions for Cox proportional hazards regression — fit_coxreg","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"Fitting functions univariate multivariate Cox regression models.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"","code":"fit_coxreg_univar(variables, data, at = list(), control = control_coxreg()) fit_coxreg_multivar(variables, data, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"variables (named list) names variables found data, passed named list corresponding time, event, arm, strata, covariates terms. arm missing variables, Cox model(s) including covariates fitted corresponding effect estimates tabulated later. data (data.frame) dataset containing variables fit models. (list numeric) candidate covariate numeric, use specify value covariate effect estimated. control (list) list parameters returned helper function control_coxreg().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"fit_coxreg_univar() returns coxreg.univar class object named list 5 elements: mod: Cox regression models fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model. : Value covariate effect estimated. fit_coxreg_multivar() returns coxreg.multivar class object named list 4 elements: mod: Cox regression model fitted survival::coxph(). data: original data frame input. control: original control input. vars: variables used model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"fit_coxreg_univar(): Fit series univariate Cox regression models given inputs. fit_coxreg_multivar(): Fit multivariate Cox regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"using fit_coxreg_univar two study arms.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_coxreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fitting functions for Cox proportional hazards regression — fit_coxreg","text":"","code":"library(survival) set.seed(1, kind = \"Mersenne-Twister\") # Testing dataset [survival::bladder]. dta_bladder <- with( data = bladder[bladder$enum < 5, ], data.frame( time = stop, status = event, armcd = as.factor(rx), covar1 = as.factor(enum), covar2 = factor( sample(as.factor(enum)), levels = 1:4, labels = c(\"F\", \"F\", \"M\", \"M\") ) ) ) labels <- c(\"armcd\" = \"ARM\", \"covar1\" = \"A Covariate Label\", \"covar2\" = \"Sex (F/M)\") formatters::var_labels(dta_bladder)[names(labels)] <- labels dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE) plot( survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder), lty = 2:4, xlab = \"Months\", col = c(\"blue1\", \"blue2\", \"blue3\", \"blue4\", \"red1\", \"red2\", \"red3\", \"red4\") ) # fit_coxreg_univar ## Cox regression: arm + 1 covariate. mod1 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = \"covar1\" ), data = dta_bladder, control = control_coxreg(conf_level = 0.91) ) ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates. mod2 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder, control = control_coxreg(conf_level = 0.91, interaction = TRUE) ) ## Cox regression: arm + 1 covariate, stratified analysis. mod3 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", strata = \"covar2\", covariates = c(\"covar1\") ), data = dta_bladder, control = control_coxreg(conf_level = 0.91) ) ## Cox regression: no arm, only covariates. mod4 <- fit_coxreg_univar( variables = list( time = \"time\", event = \"status\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder ) # fit_coxreg_multivar ## Cox regression: multivariate Cox regression. multivar_model <- fit_coxreg_multivar( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder ) # Example without treatment arm. multivar_covs_model <- fit_coxreg_multivar( variables = list( time = \"time\", event = \"status\", covariates = c(\"covar1\", \"covar2\") ), data = dta_bladder )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit for logistic regression — fit_logistic","title":"Fit for logistic regression — fit_logistic","text":"Fit (conditional) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit for logistic regression — fit_logistic","text":"","code":"fit_logistic( data, variables = list(response = \"Response\", arm = \"ARMCD\", covariates = NULL, interaction = NULL, strata = NULL), response_definition = \"response\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit for logistic regression — fit_logistic","text":"data (data.frame) data frame model fit. variables (named list string) list additional analysis variables. response_definition (string) definition event terms response. used fitting (conditional) logistic regression model left hand side formula.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit for logistic regression — fit_logistic","text":"fitted logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"model-specification","dir":"Reference","previous_headings":"","what":"Model Specification","title":"Fit for logistic regression — fit_logistic","text":"variables list needs include following elements: arm: Treatment arm variable name. response: response arm variable name. Usually 0/1 variable. covariates: either NULL (covariates) character vector covariate variable names. interaction: either NULL (interaction) string single covariate variable name already included covariates. interaction treatment arm included model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_logistic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit for logistic regression — fit_logistic","text":"","code":"library(dplyr) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>% mutate( Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), RACE = factor(RACE), SEX = factor(SEX) ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\") ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\"), interaction = \"AGE\" ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"fits Subgroup Treatment Effect Pattern logistic regression models binary (response) outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated odds ratios comparison second level vs. first one. (conditional) logistic regression model fit : response ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"","code":"fit_rsp_step(variables, data, control = c(control_step(), control_logistic()))"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"variables (named list character) list analysis variables: needs response, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_logistic().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_rsp_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup treatment effect pattern (STEP) fit for binary (response) outcome — fit_rsp_step","text":"","code":"# Testing dataset with just two treatment arms. library(survival) library(dplyr) adrs_f <- tern_ex_adrs %>% filter( PARAMCD == \"BESRSPI\", ARM %in% c(\"B: Placebo\", \"A: Drug X\") ) %>% mutate( # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations. ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")), RSP = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), SEX = factor(SEX) ) variables <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = \"AGE\", response = \"RSP\" ) # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. # We use a large enough bandwidth to avoid too small subgroups and linear separation in those. step_matrix <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = 0.9)) ) dim(step_matrix) #> [1] 39 11 head(step_matrix) #> Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,] 0.025 0 0.925 1.472755 #> [2,] 0.050 0 0.950 2.028546 #> [3,] 0.075 0 0.975 2.204086 #> [4,] 0.100 0 1.000 2.799776 #> [5,] 0.125 0 1.000 2.969998 #> [6,] 0.150 0 1.000 3.149068 #> Interval Lower Interval Upper n logor se ci_lower ci_upper #> [1,] 0.4459546 11.74529 131 1.835008 1.134560 -0.3886885 4.058703 #> [2,] 0.4459546 12.73387 134 1.895158 1.136248 -0.3318481 4.122163 #> [3,] 0.4459546 14.75099 138 1.947699 1.136969 -0.2807193 4.176116 #> [4,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 #> [5,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 #> [6,] 0.4459546 18.49236 142 1.931001 1.135866 -0.2952561 4.157258 # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different logistic regression options, including confidence level. step_matrix2 <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = NULL, degree = 1)) ) # Use a global constant model. This is helpful as a reference for the subgroup models. step_matrix3 <- fit_rsp_step( variables = variables, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L)) ) # It is also possible to use strata, i.e. use conditional logistic regression models. variables2 <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = \"AGE\", response = \"RSP\", strata = c(\"STRATA1\", \"STRATA2\") ) step_matrix4 <- fit_rsp_step( variables = variables2, data = adrs_f, control = c(control_logistic(), control_step(bandwidth = NULL)) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"fits subgroup treatment effect pattern (STEP) models survival outcome. treatment arm variable must exactly 2 levels, first one taken reference estimated hazard ratios comparison second level vs. first one. model fit : Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata) degree specified control_step().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"","code":"fit_survival_step( variables, data, control = c(control_step(), control_coxph()) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"variables (named list character) list analysis variables: needs time, event, arm, biomarker, optional covariates strata. data (data.frame) dataset containing variables summarize. control (named list) combined control list control_step() control_coxph().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"matrix class step. first part columns describe subgroup intervals used biomarker variable, including center intervals bounds. second part columns contain estimates treatment arm comparison.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"default degree 0 biomarker variable included model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/fit_survival_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subgroup treatment effect pattern (STEP) fit for survival outcome — fit_survival_step","text":"","code":"# Testing dataset with just two treatment arms. library(dplyr) adtte_f <- tern_ex_adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(forcats::fct_relevel(ARM, \"B: Placebo\")), is_event = CNSR == 0 ) labels <- c(\"ARM\" = \"Treatment Arm\", \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels variables <- list( arm = \"ARM\", biomarker = \"BMRKR1\", covariates = c(\"AGE\", \"BMRKR2\"), event = \"is_event\", time = \"AVAL\" ) # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup. step_matrix <- fit_survival_step( variables = variables, data = adtte_f ) dim(step_matrix) #> [1] 39 12 head(step_matrix) #> Percentile Center Percentile Lower Percentile Upper Interval Center #> [1,] 0.025 0 0.275 1.472755 #> [2,] 0.050 0 0.300 2.028546 #> [3,] 0.075 0 0.325 2.204086 #> [4,] 0.100 0 0.350 2.799776 #> [5,] 0.125 0 0.375 2.969998 #> [6,] 0.150 0 0.400 3.149068 #> Interval Lower Interval Upper n events loghr se ci_lower #> [1,] 0.4459546 4.182444 39 29 0.03729601 0.4243013 -0.7943192 #> [2,] 0.4459546 4.349471 43 31 0.23246622 0.3881347 -0.5282639 #> [3,] 0.4459546 4.626913 46 32 0.07952492 0.3813113 -0.6678314 #> [4,] 0.4459546 4.805767 50 35 0.09020455 0.3638368 -0.6229026 #> [5,] 0.4459546 4.929816 53 37 0.15522702 0.3477999 -0.5264483 #> [6,] 0.4459546 5.020539 57 41 0.11135760 0.3332843 -0.5418676 #> ci_upper #> [1,] 0.8689112 #> [2,] 0.9931963 #> [3,] 0.8268813 #> [4,] 0.8033117 #> [5,] 0.8369023 #> [6,] 0.7645828 # Specify different polynomial degree for the biomarker interaction to use more flexible local # models. Or specify different Cox regression options. step_matrix2 <- fit_survival_step( variables = variables, data = adtte_f, control = c(control_coxph(conf_level = 0.9), control_step(degree = 2)) ) # Use a global model with cubic interaction and only 5 points. step_matrix3 <- fit_survival_step( variables = variables, data = adtte_f, control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L)) )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a viewport tree for the forest plot — forest_viewport","text":"","code":"forest_viewport( tbl, width_row_names = NULL, width_columns = NULL, width_forest = grid::unit(1, \"null\"), gap_column = grid::unit(1, \"lines\"), gap_header = grid::unit(1, \"lines\"), mat_form = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a viewport tree for the forest plot — forest_viewport","text":"tbl (VTableTree)rtables table object. width_row_names (grid::unit) width row names. width_columns (grid::unit) width column spans. width_forest (grid::unit) width forest plot. gap_column (grid::unit) gap width columns. gap_header (grid::unit) gap width header. mat_form (MatrixPrintForm) matrix print form table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a viewport tree for the forest plot — forest_viewport","text":"viewport tree.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/forest_viewport.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a viewport tree for the forest plot — forest_viewport","text":"","code":"library(grid) tbl <- rtable( header = rheader( rrow(\"\", \"E\", rcell(\"CI\", colspan = 2)), rrow(\"\", \"A\", \"B\", \"C\") ), rrow(\"row 1\", 1, 0.8, 1.1), rrow(\"row 2\", 1.4, 0.8, 1.6), rrow(\"row 3\", 1.2, 0.8, 1.2) ) # \\donttest{ v <- forest_viewport(tbl) #> Warning: `forest_viewport()` was deprecated in tern 0.9.4. #> ℹ `g_forest` now generates `ggplot` objects. This function is no longer used #> within `tern`. #> Warning: `vp_forest_table_part()` was deprecated in tern 0.9.4. #> ℹ `g_forest` now generates `ggplot` objects. This function is no longer used #> within `tern`. #> ℹ The deprecated feature was likely used in the tern package. #> Please report the issue at #> . grid::grid.newpage() showViewport(v) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Format automatically using data significant digits — format_auto","title":"Format automatically using data significant digits — format_auto","text":"Formatting function majority default methods used analyze_vars(). non-derived values, significant digits data used (e.g. range), derived values one digits (measure location dispersion like mean, standard deviation). function can called internally \"auto\" like, example, .formats = c(\"mean\" = \"auto\"). See details see works inner function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format automatically using data significant digits — format_auto","text":"","code":"format_auto(dt_var, x_stat)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format automatically using data significant digits — format_auto","text":"dt_var (numeric) variable data statistics calculated . Used find significant digits. analyze_vars comes .df_row (see rtables::additional_fun_params), row data row splits. column split considered. x_stat (string) string indicating current statistical method used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format automatically using data significant digits — format_auto","text":"string rtables prints table cell.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Format automatically using data significant digits — format_auto","text":"internal function needed work rtables default structure format functions, .e. function(x, ...), x results statistical evaluation. can one element (e.g. .stats = \"mean_sd\").","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_auto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format automatically using data significant digits — format_auto","text":"","code":"x_todo <- c(0.001, 0.2, 0.0011000, 3, 4) res <- c(mean(x_todo[1:3]), sd(x_todo[1:3])) # x is the result coming into the formatting function -> res!! format_auto(dt_var = x_todo, x_stat = \"mean_sd\")(x = res) #> [1] \"0.06737 (0.11486)\" format_auto(x_todo, \"range\")(x = range(x_todo)) #> [1] \"0.0010 - 4.0000\" no_sc_x <- c(0.0000001, 1) format_auto(no_sc_x, \"range\")(x = no_sc_x) #> [1] \"0.0000001 - 1.0000000\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and fraction — format_count_fraction","title":"Format count and fraction — format_count_fraction","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and fraction — format_count_fraction","text":"","code":"format_count_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and fraction — format_count_fraction","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and fraction — format_count_fraction","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and fraction — format_count_fraction","text":"","code":"format_count_fraction(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"Formats count together fraction special consideration count 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"string format count (fraction %). count 0, format 0.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and percentage with fixed single decimal place — format_count_fraction_fixed_dp","text":"","code":"format_count_fraction_fixed_dp(x = c(2, 0.6667)) #> [1] \"2 (66.7%)\" format_count_fraction_fixed_dp(x = c(2, 0.5)) #> [1] \"2 (50.0%)\" format_count_fraction_fixed_dp(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"Formats count together fraction special consideration count less 10.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"x (numeric(2)) vector length 2 count fraction, respectively. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"string format count (fraction %). count less 10, count printed.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_count_fraction_lt10.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format count and fraction with special case for count < 10 — format_count_fraction_lt10","text":"","code":"format_count_fraction_lt10(x = c(275, 0.9673)) #> [1] \"275 (96.7%)\" format_count_fraction_lt10(x = c(2, 0.6667)) #> [1] \"2\" format_count_fraction_lt10(x = c(9, 1)) #> [1] \"9\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Format a single extreme value — format_extreme_values","title":"Format a single extreme value — format_extreme_values","text":"Create formatting function single extreme value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format a single extreme value — format_extreme_values","text":"","code":"format_extreme_values(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format a single extreme value — format_extreme_values","text":"digits (integer(1)) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format a single extreme value — format_extreme_values","text":"rtables formatting function uses threshold digits return formatted extreme value.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format a single extreme value — format_extreme_values","text":"","code":"format_fun <- format_extreme_values(2L) format_fun(x = 0.127) #> [1] \"0.13\" format_fun(x = Inf) #> [1] \">999.99\" format_fun(x = 0) #> [1] \"0.00\" format_fun(x = 0.009) #> [1] \"<0.01\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Format extreme values part of a confidence interval — format_extreme_values_ci","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"Formatting Function extreme values part confidence interval. Values formatted e.g. \"(xx.xx, xx.xx)\" number digits 2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"","code":"format_extreme_values_ci(digits = 2L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"digits (integer(1)) number decimal places display.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"rtables formatting function uses threshold digits return formatted extreme values confidence interval.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_extreme_values_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format extreme values part of a confidence interval — format_extreme_values_ci","text":"","code":"format_fun <- format_extreme_values_ci(2L) format_fun(x = c(0.127, Inf)) #> [1] \"(0.13, >999.99)\" format_fun(x = c(0, 0.009)) #> [1] \"(0.00, <0.01)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction and percentage — format_fraction","title":"Format fraction and percentage — format_fraction","text":"Formats fraction together ratio percent.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction and percentage — format_fraction","text":"","code":"format_fraction(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction and percentage — format_fraction","text":"x (named integer) vector elements num denom. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction and percentage — format_fraction","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction and percentage — format_fraction","text":"","code":"format_fraction(x = c(num = 2L, denom = 3L)) #> [1] \"2/3 (66.7%)\" format_fraction(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"Formats fraction together ratio percent fixed single decimal place. Includes trailing zero case whole number percentages always keep one decimal place.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"x (named integer) vector elements num denom. ... used. Required rtables interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"string format num / denom (ratio %). num 0, format num / denom.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_fixed_dp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction and percentage with fixed single decimal place — format_fraction_fixed_dp","text":"","code":"format_fraction_fixed_dp(x = c(num = 1L, denom = 2L)) #> [1] \"1/2 (50.0%)\" format_fraction_fixed_dp(x = c(num = 1L, denom = 4L)) #> [1] \"1/4 (25.0%)\" format_fraction_fixed_dp(x = c(num = 0L, denom = 3L)) #> [1] \"0/3\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Format fraction with lower threshold — format_fraction_threshold","title":"Format fraction with lower threshold — format_fraction_threshold","text":"Formats fraction second element input x fraction. applies lower threshold, just stated fraction smaller .","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format fraction with lower threshold — format_fraction_threshold","text":"","code":"format_fraction_threshold(threshold)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format fraction with lower threshold — format_fraction_threshold","text":"threshold (proportion) lower threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format fraction with lower threshold — format_fraction_threshold","text":"rtables formatting function takes numeric input x second element fraction formatted. fraction equal threshold, displayed percentage. positive threshold, returns, e.g. \"<1\" threshold 0.01. zero, just \"0\" returned.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_fraction_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format fraction with lower threshold — format_fraction_threshold","text":"","code":"format_fun <- format_fraction_threshold(0.05) format_fun(x = c(20, 0.1)) #> [1] 10 format_fun(x = c(2, 0.01)) #> [1] \"<5\" format_fun(x = c(0, 0)) #> [1] \"0\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":null,"dir":"Reference","previous_headings":"","what":"Format numeric values by significant figures — format_sigfig","title":"Format numeric values by significant figures — format_sigfig","text":"Format numeric values print specified number significant figures.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format numeric values by significant figures — format_sigfig","text":"","code":"format_sigfig(sigfig, format = \"xx\", num_fmt = \"fg\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format numeric values by significant figures — format_sigfig","text":"sigfig (integer(1)) number significant figures display. format (string) format label (string) apply printing value. Decimal places string ignored favor formatting significant figures. Formats options : \"xx\", \"xx / xx\", \"(xx, xx)\", \"xx - xx\", \"xx (xx)\". num_fmt (string) numeric format modifiers apply value. Defaults \"fg\" standard significant figures formatting - fixed (non-scientific notation) format (\"f\") sigfig equal number significant figures instead decimal places (\"g\"). See formatC() format argument options.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format numeric values by significant figures — format_sigfig","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_sigfig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format numeric values by significant figures — format_sigfig","text":"","code":"fmt_3sf <- format_sigfig(3) fmt_3sf(1.658) #> [1] \"1.66\" fmt_3sf(1e1) #> [1] \"10.0\" fmt_5sf <- format_sigfig(5) fmt_5sf(0.57) #> [1] \"0.57000\" fmt_5sf(0.000025645) #> [1] \"0.000025645\""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":null,"dir":"Reference","previous_headings":"","what":"Format XX as a formatting function — format_xx","title":"Format XX as a formatting function — format_xx","text":"Translate string x dots interpreted number place holders, others formatting elements.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format XX as a formatting function — format_xx","text":"","code":"format_xx(str)"},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format XX as a formatting function — format_xx","text":"str (string) template.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format XX as a formatting function — format_xx","text":"rtables formatting function.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/format_xx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format XX as a formatting function — format_xx","text":"","code":"test <- list(c(1.658, 0.5761), c(1e1, 785.6)) z <- format_xx(\"xx (xx.x)\") sapply(test, z) #> [1] \"2 (0.6)\" \"10 (785.6)\" z <- format_xx(\"xx.x - xx.x\") sapply(test, z) #> [1] \"1.7 - 0.6\" \"10 - 785.6\" z <- format_xx(\"xx.x, incl. xx.x% NE\") sapply(test, z) #> [1] \"1.7, incl. 0.6% NE\" \"10, incl. 785.6% NE\""},{"path":"https://insightsengineering.github.io/tern/main/reference/formatting_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatting functions — formatting_functions","title":"Formatting functions — formatting_functions","text":"See list formatting functions created tern work rtables. available formats can listed via formatters::list_valid_format_labels(). Additional custom formats can created via formatters::sprintf_format() function.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a forest plot from an rtable — g_forest","text":"","code":"g_forest( tbl, col_x = attr(tbl, \"col_x\"), col_ci = attr(tbl, \"col_ci\"), vline = 1, forest_header = attr(tbl, \"forest_header\"), xlim = c(0.1, 10), logx = TRUE, x_at = c(0.1, 1, 10), width_row_names = lifecycle::deprecated(), width_columns = NULL, width_forest = lifecycle::deprecated(), lbl_col_padding = 0, rel_width_forest = 0.25, font_size = 12, col_symbol_size = attr(tbl, \"col_symbol_size\"), col = getOption(\"ggplot2.discrete.colour\")[1], ggtheme = NULL, as_list = FALSE, gp = lifecycle::deprecated(), draw = lifecycle::deprecated(), newpage = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a forest plot from an rtable — g_forest","text":"tbl (VTableTree)rtables table least one column single value one column 2 values. col_x (integer(1) NULL) column index estimator. default tries get tbl attribute col_x, otherwise needs manually specified. NULL, points excluded forest plot. col_ci (integer(1) NULL) column index confidence intervals. default tries get tbl attribute col_ci, otherwise needs manually specified. NULL, lines excluded forest plot. vline (numeric(1) NULL) x coordinate vertical line, NULL line omitted. forest_header (character(2)) text displayed left right vline, respectively. vline = NULL forest_header printed. default tries get tbl attribute forest_header. NULL, defaults extracted table possible, set \"Comparison\\nBetter\" \"Treatment\\nBetter\" . xlim (numeric(2)) limits x axis. logx (flag) show x-values logarithm scale. x_at (numeric) x-tick locations, NULL, x_at set vline xlim values. width_row_names Please use lbl_col_padding argument instead. width_columns (numeric) vector column widths. element's position colwidths corresponds column tbl position. NULL, column widths calculated according maximum number characters per column. width_forest Please use rel_width_forest argument instead. lbl_col_padding (numeric) additional padding use calculating spacing first (label) column second column tbl. colwidths specified, width first column becomes colwidths[1] + lbl_col_padding. Defaults 0. rel_width_forest (proportion) proportion total width allocate forest plot. Relative width table 1 - rel_width_forest. as_list = TRUE, parameter ignored. font_size (numeric(1)) font size. col_symbol_size (numeric NULL) column index tbl containing data used determine relative size estimator plot symbol. Typically, symbol size proportional sample size used calculate estimator. NULL, symbol size used subgroups. default tries get tbl attribute col_symbol_size, otherwise needs manually specified. col (character) color(s). ggtheme (theme) graphical theme provided ggplot2 control styling plot. as_list (flag) whether two ggplot objects returned list. TRUE, named list two elements, table plot, returned. FALSE (default) table forest plot printed side--side via cowplot::plot_grid(). gp g_forest now generated ggplot object. argument longer used. draw g_forest now generated ggplot object. argument longer used. newpage g_forest now generated ggplot object. argument longer used.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a forest plot from an rtable — g_forest","text":"ggplot forest plot table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a forest plot from an rtable — g_forest","text":"Given rtables::rtable() object least one column single value one column 2 values, converts table ggplot2::ggplot() object generates accompanying forest plot. table forest plot printed side--side.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_forest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a forest plot from an rtable — g_forest","text":"","code":"library(dplyr) library(forcats) library(nestcolor) adrs <- tern_ex_adrs n_records <- 20 adrs_labels <- formatters::var_labels(adrs, fill = TRUE) adrs <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% slice(seq_len(n_records)) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs) <- c(adrs_labels, \"Response\") df <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"STRATA2\")), data = adrs ) # Full commonly used response table. tbl <- basic_table() %>% tabulate_rsp_subgroups(df) g_forest(tbl) # Odds ratio only table. tbl_or <- basic_table() %>% tabulate_rsp_subgroups(df, vars = c(\"n_tot\", \"or\", \"ci\")) g_forest( tbl_or, forest_header = c(\"Comparison\\nBetter\", \"Treatment\\nBetter\") ) # Survival forest plot example. adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = TRUE) adtte_f <- adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\"), SEX %in% c(\"M\", \"F\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")), SEX = droplevels(SEX), AVALU = as.character(AVALU), is_event = CNSR == 0 ) labels <- list( \"ARM\" = adtte_labels[\"ARM\"], \"SEX\" = adtte_labels[\"SEX\"], \"AVALU\" = adtte_labels[\"AVALU\"], \"is_event\" = \"Event Flag\" ) formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels) df <- extract_survival_subgroups( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) table_hr <- basic_table() %>% tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1]) g_forest(table_hr) # Works with any `rtable`. tbl <- rtable( header = c(\"E\", \"CI\", \"N\"), rrow(\"\", 1, c(.8, 1.2), 200), rrow(\"\", 1.2, c(1.1, 1.4), 50) ) g_forest( tbl = tbl, col_x = 1, col_ci = 2, xlim = c(0.5, 2), x_at = c(0.5, 1, 2), col_symbol_size = 3 ) tbl <- rtable( header = rheader( rrow(\"\", rcell(\"A\", colspan = 2)), rrow(\"\", \"c1\", \"c2\") ), rrow(\"row 1\", 1, c(.8, 1.2)), rrow(\"row 2\", 1.2, c(1.1, 1.4)) ) g_forest( tbl = tbl, col_x = 1, col_ci = 2, xlim = c(0.5, 2), x_at = c(0.5, 1, 2), vline = 1, forest_header = c(\"Hello\", \"World\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Individual patient plots — g_ipp","title":"Individual patient plots — g_ipp","text":"Line plot(s) displaying trend patients' parameter values time rendered. Patients' individual baseline values can added plot(s) reference.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Individual patient plots — g_ipp","text":"","code":"g_ipp( df, xvar, yvar, xlab, ylab, id_var = \"USUBJID\", title = \"Individual Patient Plots\", subtitle = \"\", caption = NULL, add_baseline_hline = FALSE, yvar_baseline = \"BASE\", ggtheme = nestcolor::theme_nest(), plotting_choices = c(\"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\"), max_obs_per_plot = 4, col = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Individual patient plots — g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (string) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. plotting_choices (string) specifies options displaying plots. Must one \"all_in_one\", \"split_by_max_obs\", \"separate_by_obs\". max_obs_per_plot (integer(1)) number observations plotted one plot. Ignored plotting_choices \"separate_by_obs\". col (character) line colors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Individual patient plots — g_ipp","text":"ggplot object list ggplot objects.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Individual patient plots — g_ipp","text":"g_ipp(): Plotting function individual patient plots , depending user preference, renders single graphic compiles list graphics show trends individual's parameter values time.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Individual patient plots — g_ipp","text":"","code":"library(dplyr) library(nestcolor) # Select a small sample of data to plot. adlb <- tern_ex_adlb %>% filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>% slice(1:36) plot_list <- g_ipp( df = adlb, xvar = \"AVISIT\", yvar = \"AVAL\", xlab = \"Visit\", ylab = \"SGOT/ALT (U/L)\", title = \"Individual Patient Plots\", add_baseline_hline = TRUE, plotting_choices = \"split_by_max_obs\", max_obs_per_plot = 5 ) plot_list #> [[1]] #> #> [[2]] #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier plot — g_km","title":"Kaplan-Meier plot — g_km","text":"survival model, graphic rendered along tabulated annotation including number patient risk given time median survival per group.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier plot — g_km","text":"","code":"g_km( df, variables, control_surv = control_surv_timepoint(), col = NULL, lty = NULL, lwd = 0.5, censor_show = TRUE, pch = 3, size = 2, max_time = NULL, xticks = NULL, xlab = \"Days\", yval = c(\"Survival\", \"Failure\"), ylab = paste(yval, \"Probability\"), ylim = NULL, title = NULL, footnotes = NULL, font_size = 10, ci_ribbon = FALSE, annot_at_risk = TRUE, annot_at_risk_title = TRUE, annot_surv_med = TRUE, annot_coxph = FALSE, annot_stats = NULL, annot_stats_vlines = FALSE, control_coxph_pw = control_coxph(), ref_group_coxph = NULL, control_annot_surv_med = control_surv_med_annot(), control_annot_coxph = control_coxph_annot(), legend_pos = NULL, rel_height_plot = 0.75, ggtheme = NULL, as_list = FALSE, draw = lifecycle::deprecated(), newpage = lifecycle::deprecated(), gp = lifecycle::deprecated(), vp = lifecycle::deprecated(), name = lifecycle::deprecated(), annot_coxph_ref_lbls = lifecycle::deprecated(), position_coxph = lifecycle::deprecated(), position_surv_med = lifecycle::deprecated(), width_annots = lifecycle::deprecated() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier plot — g_km","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strata (character NULL) variable names indicating stratification factors. control_surv (list) parameters comparison details, specified using helper function control_surv_timepoint(). possible parameter options : conf_level (proportion) confidence level interval survival rate. conf_type (string)\"plain\" (default), \"log\", \"log-log\" confidence interval type, see survival::survfit(). Note option \"none\" longer supported. col (character) lines colors. Length vector equal number strata survival::survfit(). lty (numeric) line type. vector given, length equal number strata survival::survfit(). lwd (numeric) line width. vector given, length equal number strata survival::survfit(). censor_show (flag) whether show censored observations. pch (string) name symbol character use point symbol indicate censored cases. size (numeric(1)) size censored point symbols. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL). xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. xlab (string) x-axis label. yval (string) type plot, plotted y-axis. Options Survival (default) Failure probability. ylab (string) y-axis label. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. title (string) plot title. footnotes (string) plot footnotes. font_size (numeric(1)) font size use text. ci_ribbon (flag) whether confidence interval drawn around Kaplan-Meier curve. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE. annot_surv_med (flag) compute add annotation table Kaplan-Meier curve estimating median survival time per group. annot_coxph (flag) whether add annotation table survival::coxph() model. annot_stats (string NULL) statistics annotations add plot. Options median (median survival follow-time) min (minimum survival follow-time). annot_stats_vlines (flag) add vertical lines corresponding statistics specified annot_stats. annot_stats NULL lines added. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. ref_group_coxph (string NULL) level arm variable use reference group calculations annot_coxph table. NULL (default), uses first level arm variable. control_annot_surv_med (list) parameters control position size annotation table added plot annot_surv_med = TRUE, specified using control_surv_med_annot() function. Parameter options : x, y, w, h, fill. See control_surv_med_annot() details. control_annot_coxph (list) parameters control position size annotation table added plot annot_coxph = TRUE, specified using control_coxph_annot() function. Parameter options : x, y, w, h, fill, ref_lbls. See control_coxph_annot() details. legend_pos (numeric(2) NULL) vector containing x- y-coordinates, respectively, legend position relative KM plot area. NULL (default), legend positioned bottom right corner plot, middle right plot needed prevent overlapping. rel_height_plot (proportion) proportion total figure height allocate Kaplan-Meier plot. Relative height patients risk table 1 - rel_height_plot. annot_at_risk = FALSE as_list = TRUE, parameter ignored. ggtheme (theme) graphical theme provided ggplot2 format Kaplan-Meier plot. as_list (flag) whether two ggplot objects returned list annot_at_risk = TRUE. TRUE, named list two elements, plot table, returned. FALSE (default) patients risk table printed plot via cowplot::plot_grid(). draw function longer generates grob objects. newpage function longer generates grob objects. gp function longer generates grob objects. vp function longer generates grob objects. name function longer generates grob objects. annot_coxph_ref_lbls Please use ref_lbls element control_annot_coxph instead. position_coxph Please use x y elements control_annot_coxph instead. position_surv_med Please use x y elements control_annot_surv_med instead. width_annots Please use w element control_annot_surv_med (surv_med) control_annot_coxph (coxph).\"","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier plot — g_km","text":"ggplot Kaplan-Meier plot (optionally) summary table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_km.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier plot — g_km","text":"","code":"library(dplyr) library(nestcolor) df <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) variables <- list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\") # Basic examples g_km(df = df, variables = variables) g_km(df = df, variables = variables, yval = \"Failure\") # Examples with customization parameters applied g_km( df = df, variables = variables, control_surv = control_surv_timepoint(conf_level = 0.9), col = c(\"grey25\", \"grey50\", \"grey75\"), annot_at_risk_title = FALSE, lty = 1:3, font_size = 8 ) g_km( df = df, variables = variables, annot_stats = c(\"min\", \"median\"), annot_stats_vlines = TRUE, max_time = 3000, ggtheme = ggplot2::theme_minimal() ) # Example with pairwise Cox-PH analysis annotation table, adjusted annotation tables g_km( df = df, variables = variables, annot_coxph = TRUE, control_coxph = control_coxph(pval_method = \"wald\", ties = \"exact\", conf_level = 0.99), control_annot_coxph = control_coxph_annot(x = 0.26, w = 0.35), control_annot_surv_med = control_surv_med_annot(x = 0.8, y = 0.9, w = 0.35) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Line plot with optional table — g_lineplot","title":"Line plot with optional table — g_lineplot","text":"Line plot optional table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Line plot with optional table — g_lineplot","text":"","code":"g_lineplot( df, alt_counts_df = NULL, variables = control_lineplot_vars(), mid = \"mean\", interval = \"mean_ci\", whiskers = c(\"mean_ci_lwr\", \"mean_ci_upr\"), table = NULL, sfun = s_summary, ..., mid_type = \"pl\", mid_point_size = 2, position = ggplot2::position_dodge(width = 0.4), legend_title = NULL, legend_position = \"bottom\", ggtheme = nestcolor::theme_nest(), xticks = NULL, xlim = NULL, ylim = NULL, x_lab = obj_label(df[[variables[[\"x\"]]]]), y_lab = NULL, y_lab_add_paramcd = TRUE, y_lab_add_unit = TRUE, title = \"Plot of Mean and 95% Confidence Limits by Visit\", subtitle = \"\", subtitle_add_paramcd = TRUE, subtitle_add_unit = TRUE, caption = NULL, table_format = NULL, table_labels = NULL, table_font_size = 3, errorbar_width = 0.45, newpage = lifecycle::deprecated(), col = NULL, linetype = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Line plot with optional table — g_lineplot","text":"df (data.frame) data set containing analysis variables. alt_counts_df (data.frame NULL) data set used () counts objects groups stratification. variables (named character) vector variable names df include: x (string) name x-axis variable. y (string) name y-axis variable. group_var (string NULL) name grouping variable (strata), .e. treatment arm. Can NA indicate lack groups. subject_var (string NULL) name subject variable. applies group_var NULL. paramcd (string NA) name variable parameter's code. Used y-axis label plot's subtitle. Can NA paramcd added y-axis label subtitle. y_unit (string NA) name variable units y. Used y-axis label plot's subtitle. Can NA y unit added y-axis label subtitle. facet_var (string NA) name secondary grouping variable used plot faceting, .e. treatment arm. Can NA indicate lack groups. mid (character NULL) names statistics plotted midpoints. statistics indicated mid variable must present object returned sfun, double numeric type vector length one. interval (character NULL) names statistics plotted intervals. statistics indicated interval variable must present object returned sfun, double numeric type vector length two. Set interval = NULL intervals added plot. whiskers (character) names interval whiskers plotted. Names must match names list element interval returned sfun (e.g. mean_ci_lwr element sfun(x)[[\"mean_ci\"]]). possible specify one whisker , suppress whiskers setting interval = NULL. table (character NULL) names statistics displayed table plot. statistics indicated table variable must present object returned sfun. sfun (function) function compute values required statistics. must return named list atomic vectors. names list elements refer names statistics used mid, interval, table. must able accept input vector data statistics computed. ... optional arguments sfun. mid_type (string) controls type mid plot, can point (\"p\"), line (\"l\"), point line (\"pl\"). mid_point_size (numeric(1)) font size mid plot points. position (character call) geom element position adjustment, either string, result call position adjustment function. legend_title (string) legend title. legend_position (string) position plot legend (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector). ggtheme (theme) graphical theme provided ggplot2 control styling plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis, use variables$x numeric. NULL (default), labeling::extended() used determine optimal tick positions x-axis. variables$x numeric, argument ignored. xlim (numeric(2)) vector containing lower upper limits x-axis, respectively. NULL (default), default scale range used. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. x_lab (string NULL) x-axis label. NULL label added. y_lab (string NULL) y-axis label. NULL label added. y_lab_add_paramcd (flag) whether paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added y-axis label (y_lab). y_lab_add_unit (flag) whether y-axis unit, .e. unique(df[[variables[\"y_unit\"]]]) added y-axis label (y_lab). title (string) plot title. subtitle (string) plot subtitle. subtitle_add_paramcd (flag) whether paramcd, .e. unique(df[[variables[\"paramcd\"]]]) added plot's subtitle (subtitle). subtitle_add_unit (flag) whether y-axis unit, .e. unique(df[[variables[\"y_unit\"]]]) added plot's subtitle (subtitle). caption (string) optional caption plot. table_format (named character NULL) format patterns descriptive statistics used (optional) table appended plot. passed directly h_format_row function format parameter. Names table_format must match names statistics returned sfun function. table_labels (named character NULL) labels descriptive statistics used (optional) table appended plot. Names table_labels must match names statistics returned sfun function. table_font_size (numeric(1)) font size text table. errorbar_width (numeric(1)) width error bars. newpage used. col (character) color(s). See ?ggplot2::aes_colour_fill_alpha example values. linetype (character) line type(s). See ?ggplot2::aes_linetype_size_shape example values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Line plot with optional table — g_lineplot","text":"ggplot line plot (statistics table applicable).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_lineplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Line plot with optional table — g_lineplot","text":"","code":"library(nestcolor) adsl <- tern_ex_adsl adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == \"Y\", PARAMCD == \"ALT\", AVISIT != \"SCREENING\") adlb$AVISIT <- droplevels(adlb$AVISIT) adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min)) # Mean with CI g_lineplot(adlb, adsl, subtitle = \"Laboratory Test:\") # Mean with CI, no stratification with group_var g_lineplot(adlb, variables = control_lineplot_vars(group_var = NA)) # Mean, upper whisker of CI, no group_var(strata) counts N g_lineplot( adlb, whiskers = \"mean_ci_upr\", title = \"Plot of Mean and Upper 95% Confidence Limit by Visit\" ) # Median with CI g_lineplot( adlb, adsl, mid = \"median\", interval = \"median_ci\", whiskers = c(\"median_ci_lwr\", \"median_ci_upr\"), title = \"Plot of Median and 95% Confidence Limits by Visit\" ) # Mean, +/- SD g_lineplot(adlb, adsl, interval = \"mean_sdi\", whiskers = c(\"mean_sdi_lwr\", \"mean_sdi_upr\"), title = \"Plot of Median +/- SD by Visit\" ) # Mean with CI plot with stats table g_lineplot(adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\")) # Mean with CI, table and customized confidence level g_lineplot( adlb, adsl, table = c(\"n\", \"mean\", \"mean_ci\"), control = control_analyze_vars(conf_level = 0.80), title = \"Plot of Mean and 80% Confidence Limits by Visit\" ) # Mean with CI, table, filtered data adlb_f <- dplyr::filter(adlb, ARMCD != \"ARM A\" | AVISIT == \"BASELINE\") g_lineplot(adlb_f, table = c(\"n\", \"mean\"))"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a STEP graph — g_step","title":"Create a STEP graph — g_step","text":"Based STEP results, creates ggplot graph showing estimated HR along continuous biomarker value subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a STEP graph — g_step","text":"","code":"g_step( df, use_percentile = \"Percentile Center\" %in% names(df), est = list(col = \"blue\", lty = 1), ci_ribbon = list(fill = getOption(\"ggplot2.discrete.colour\")[1], alpha = 0.5), col = getOption(\"ggplot2.discrete.colour\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a STEP graph — g_step","text":"df (tibble) result tidy.step(). use_percentile (flag) whether use percentiles x axis actual biomarker values. est (named list)col lty settings estimate line. ci_ribbon (named list NULL)fill alpha settings confidence interval ribbon area, NULL plot CI ribbon. col (character) color(s).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a STEP graph — g_step","text":"ggplot STEP graph.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/g_step.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a STEP graph — g_step","text":"","code":"library(nestcolor) library(survival) lung$sex <- factor(lung$sex) # Survival example. vars <- list( time = \"time\", event = \"status\", arm = \"sex\", biomarker = \"age\" ) step_matrix <- fit_survival_step( variables = vars, data = lung, control = c(control_coxph(), control_step(num_points = 10, degree = 2)) ) step_data <- broom::tidy(step_matrix) # Default plot. g_step(step_data) # Add the reference 1 horizontal line. library(ggplot2) g_step(step_data) + ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2) # Use actual values instead of percentiles, different color for estimate and no CI, # use log scale for y axis. g_step( step_data, use_percentile = FALSE, est = list(col = \"blue\", lty = 1), ci_ribbon = NULL ) + scale_y_log10() # Adding another curve based on additional column. step_data$extra <- exp(step_data$`Percentile Center`) g_step(step_data) + ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = \"green\") # Response example. vars <- list( response = \"status\", arm = \"sex\", biomarker = \"age\" ) step_matrix <- fit_rsp_step( variables = vars, data = lung, control = c( control_logistic(response_definition = \"I(response == 2)\"), control_step() ) ) step_data <- broom::tidy(step_matrix) g_step(step_data)"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":null,"dir":"Reference","previous_headings":"","what":"Horizontal waterfall plot — g_waterfall","title":"Horizontal waterfall plot — g_waterfall","text":"basic waterfall plot visualizes quantity height ordered value markup.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Horizontal waterfall plot — g_waterfall","text":"","code":"g_waterfall( height, id, col_var = NULL, col = getOption(\"ggplot2.discrete.colour\"), xlab = NULL, ylab = NULL, col_legend_title = NULL, title = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Horizontal waterfall plot — g_waterfall","text":"height (numeric) vector containing values plotted waterfall bars. id (character) vector containing identifiers use x-axis label waterfall bars. col_var (factor, character, NULL) categorical variable bar coloring. NULL default. col (character) color(s). xlab (string) x label. Default \"ID\". ylab (string) y label. Default \"Value\". col_legend_title (string) text displayed legend title. title (string) text displayed plot title.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Horizontal waterfall plot — g_waterfall","text":"ggplot waterfall plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/g_waterfall.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Horizontal waterfall plot — g_waterfall","text":"","code":"library(dplyr) library(nestcolor) g_waterfall(height = c(3, 5, -1), id = letters[1:3]) g_waterfall( height = c(3, 5, -1), id = letters[1:3], col_var = letters[1:3] ) adsl_f <- tern_ex_adsl %>% select(USUBJID, STUDYID, ARM, ARMCD, SEX) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"OVRINV\") %>% mutate(pchg = rnorm(n(), 10, 50)) adrs_f <- head(adrs_f, 30) adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ] head(adrs_f) #> # A tibble: 5 × 30 #> STUDYID COUNTRY SITEID SUBJID AGE SEX ARMCD ARM ACTARMCD ACTARM RACE #> #> 1 AB12345 BRA BRA-1 id-105 37.8 F ARM A A: Drug… ARM A A: Dr… ASIAN #> 2 AB12345 BRA BRA-1 id-171 29.8 F ARM B B: Plac… ARM B B: Pl… ASIAN #> 3 AB12345 BRA BRA-1 id-177 38.9 F ARM B B: Plac… ARM B B: Pl… ASIAN #> 4 AB12345 BRA BRA-1 id-23 41.3 F ARM A A: Drug… ARM A A: Dr… AMER… #> 5 AB12345 BRA BRA-1 id-59 26.7 F ARM A A: Drug… ARM A A: Dr… ASIAN #> # ℹ 19 more variables: TRTSDTM , TRTEDTM , EOSDY , #> # STRATA1 , STRATA2 , BMRKR1 , BMRKR2 , REGION1 , #> # SAFFL , USUBJID , AVISIT , PARAMCD , PARAM , #> # AVALC , AVAL , ADTM , AVISITN , DTHFL , #> # pchg g_waterfall( height = adrs_f$pchg, id = adrs_f$USUBJID, col_var = adrs_f$AVALC ) g_waterfall( height = adrs_f$pchg, id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID), col_var = adrs_f$SEX ) g_waterfall( height = adrs_f$pchg, id = paste(\"asdfdsfdsfsd\", adrs_f$USUBJID), xlab = \"ID\", ylab = \"Percentage Change\", title = \"Waterfall plot\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Utility function to return a named list of covariate names — get_covariates","title":"Utility function to return a named list of covariate names — get_covariates","text":"Utility function return named list covariate names","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Utility function to return a named list of covariate names — get_covariates","text":"","code":"get_covariates(covariates)"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Utility function to return a named list of covariate names — get_covariates","text":"covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Utility function to return a named list of covariate names — get_covariates","text":"named list character vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":null,"dir":"Reference","previous_headings":"","what":"Smooth function with optional grouping — get_smooths","title":"Smooth function with optional grouping — get_smooths","text":"produces loess smoothed estimates y Student confidence intervals.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Smooth function with optional grouping — get_smooths","text":"","code":"get_smooths(df, x, y, groups = NULL, level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Smooth function with optional grouping — get_smooths","text":"df (data.frame) data set containing analysis variables. x (string) x column name. y (string) y column name. groups (character NULL) vector optional grouping variables names. level (proportion) level confidence interval use (0.95 default).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/get_smooths.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Smooth function with optional grouping — get_smooths","text":"data.frame original x, smoothed y, ylow, yhigh, optional groups variables formatted factor type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert list of groups to a data frame — groups_list_to_df","title":"Convert list of groups to a data frame — groups_list_to_df","text":"converts list group levels data frame format expected rtables::add_combo_levels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert list of groups to a data frame — groups_list_to_df","text":"","code":"groups_list_to_df(groups_list)"},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert list of groups to a data frame — groups_list_to_df","text":"groups_list (named list character) specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert list of groups to a data frame — groups_list_to_df","text":"tibble required format.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/groups_list_to_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert list of groups to a data frame — groups_list_to_df","text":"","code":"grade_groups <- list( \"Any Grade (%)\" = c(\"1\", \"2\", \"3\", \"4\", \"5\"), \"Grade 3-4 (%)\" = c(\"3\", \"4\"), \"Grade 5 (%)\" = \"5\" ) groups_list_to_df(grade_groups) #> # A tibble: 3 × 4 #> valname label levelcombo exargs #> #> 1 AnyGrade Any Grade (%) #> 2 Grade34 Grade 3-4 (%) #> 3 Grade5 Grade 5 (%) "},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"Helper function prepare ADLB data frame used input count_abnormal_by_worst_grade(). following pre-processing steps applied: adlb filtered variable avisit include post-baseline visits. adlb filtered variables worst_flag_low worst_flag_high worst grades (either direction) included. standard lab grade variable atoxgr, following two variables derived added adlb: grade direction variable (e.g. GRADE_DIR). variable takes value \"HIGH\" atoxgr > 0, \"LOW\" atoxgr < 0, \"ZERO\" otherwise. toxicity grade variable (e.g. GRADE_ANL) negative values atoxgr replaced absolute values. Unused factor levels dropped adlb via droplevels().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"","code":"h_adlb_abnormal_by_worst_grade( adlb, atoxgr = \"ATOXGR\", avisit = \"AVISIT\", worst_flag_low = \"WGRLOFL\", worst_flag_high = \"WGRHIFL\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"adlb (data.frame) ADLB data frame. atoxgr (string) name analysis toxicity grade variable. must factor variable. avisit (string) name analysis visit variable. worst_flag_low (string) name worst low lab grade flag variable. variable set \"Y\" indicating records worst low lab grades. worst_flag_high (string) name worst high lab grade flag variable. variable set \"Y\" indicating records worst high lab grades.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"h_adlb_abnormal_by_worst_grade() returns adlb data frame two new variables: GRADE_DIR GRADE_ANL.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_abnormal_by_worst_grade.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare ADLB for count_abnormal_by_worst_grade() — h_adlb_abnormal_by_worst_grade","text":"","code":"h_adlb_abnormal_by_worst_grade(tern_ex_adlb) %>% dplyr::select(ATOXGR, GRADE_DIR, GRADE_ANL) %>% head(10) #> # A tibble: 10 × 3 #> ATOXGR GRADE_DIR GRADE_ANL #> #> 1 -3 LOW 3 #> 2 0 ZERO 0 #> 3 0 ZERO 0 #> 4 2 HIGH 2 #> 5 0 ZERO 0 #> 6 0 ZERO 0 #> 7 -4 LOW 4 #> 8 1 HIGH 1 #> 9 -1 LOW 1 #> 10 0 ZERO 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"Helper function prepare df generate patient count shift table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"","code":"h_adlb_worsen( adlb, worst_flag_low = NULL, worst_flag_high = NULL, direction_var )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"adlb (data.frame) ADLB data frame. worst_flag_low (named vector) worst low post-baseline lab grade flag variable. See implemented following examples. worst_flag_high (named vector) worst high post-baseline lab grade flag variable. See implemented following examples. direction_var (string) name direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"h_adlb_worsen() returns adlb data.frame containing worst labs specified according worst_flag_low worst_flag_high direction specified according direction_var. instance, lab needed low direction , records flagged worst_flag_low selected. lab needed low high directions, worst low records selected low direction, worst high record selected high direction.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adlb_worsen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare ADLB with worst labs — h_adlb_worsen","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"Helper function merges ADSL ADLB datasets missing lab test records inserted output dataset. Remember na_level must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"","code":"h_adsl_adlb_merge_using_worst_flag( adsl, adlb, worst_flag = c(WGRHIFL = \"Y\"), by_visit = FALSE, no_fillin_visits = c(\"SCREENING\", \"BASELINE\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"adsl (data.frame) ADSL data frame. adlb (data.frame) ADLB data frame. worst_flag (named character) worst post-baseline lab flag variable. See implemented following examples. by_visit (flag) defaults FALSE generate worst grade per patient. worst grade per patient per visit specified worst_flag, by_visit TRUE generate worst grade patient per visit. no_fillin_visits (named character) visits considered post-baseline worst toxicity grade. Defaults c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"df containing variables shared adlb adsl along variables PARAM, PARAMCD, ATOXGR, BTOXGR relevant analysis. Optionally, AVISIT AVISITN included by_visit = TRUE no_fillin_visits = c(\"SCREENING\", \"BASELINE\").","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"result data missing records created following situations: Patients present adsl lab data adlb (baseline post-baseline). Patients post-baseline lab values. Patients without post-baseline values flagged worst.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_adsl_adlb_merge_using_worst_flag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for deriving analysis datasets for select laboratory tables — h_adsl_adlb_merge_using_worst_flag","text":"","code":"# `h_adsl_adlb_merge_using_worst_flag` adlb_out <- h_adsl_adlb_merge_using_worst_flag( tern_ex_adsl, tern_ex_adlb, worst_flag = c(\"WGRHIFL\" = \"Y\") ) # `h_adsl_adlb_merge_using_worst_flag` by visit example adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag( tern_ex_adsl, tern_ex_adlb, worst_flag = c(\"WGRLOVFL\" = \"Y\"), by_visit = TRUE )"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to return results of a linear model — h_ancova","text":"","code":"h_ancova(.var, .df_row, variables, interaction_item = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to return results of a linear model — h_ancova","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". interaction_item (string NULL) name variable interactions arm. interaction needed, default option NULL.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to return results of a linear model — h_ancova","text":"summary linear model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to return results of a linear model — h_ancova","text":"","code":"h_ancova( .var = \"Sepal.Length\", .df_row = iris, variables = list(arm = \"Species\", covariates = c(\"Petal.Length * Petal.Width\", \"Sepal.Width\")) ) #> Species emmean SE df lower.CL upper.CL #> setosa 6.15 0.3371 143 5.49 6.82 #> versicolor 5.72 0.0668 143 5.59 5.85 #> virginica 5.41 0.1488 143 5.11 5.70 #> #> Confidence level used: 0.95"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Helper function s_count_occurrences_by_grade() insert grade groupings list individual grade frequencies. order final result follows order grade_groups. elements -grade group (), .e. grade group equal refs moved end. Grade groups names must unique.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups( grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"grade_groups (named list character) list containing groupings grades. refs (named list numeric) named list name corresponds reference grade level entry represents count. remove_single (flag)TRUE include elements one-element grade groups output list; case grade groups names included output. only_grade_groups set TRUE argument ignored. only_grade_groups (flag) whether specified grade groups included, individual grade rows removed (TRUE), grades grade groups displayed (FALSE).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"Formatted list grade groupings.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_append_grade_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_occurrences_by_grade() — h_append_grade_groups","text":"","code":"h_append_grade_groups( list( \"Any Grade\" = as.character(1:5), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-4\" = c(\"3\", \"4\") ), list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #> #> $`Grade 1-2` #> [1] 30 #> #> $`1` #> [1] 10 #> #> $`2` #> [1] 20 #> #> $`Grade 3-4` #> [1] 70 #> #> $`3` #> [1] 30 #> #> $`4` #> [1] 40 #> #> $`5` #> [1] 50 #> h_append_grade_groups( list( \"Any Grade\" = as.character(5:1), \"Grade A\" = \"5\", \"Grade B\" = c(\"4\", \"3\") ), list(\"1\" = 10, \"2\" = 20, \"3\" = 30, \"4\" = 40, \"5\" = 50) ) #> $`Any Grade` #> [1] 150 #> #> $`Grade A` #> [1] 50 #> #> $`Grade B` #> [1] 70 #> #> $`4` #> [1] 40 #> #> $`3` #> [1] 30 #> #> $`2` #> [1] 20 #> #> $`1` #> [1] 10 #> h_append_grade_groups( list( \"Any Grade\" = as.character(1:5), \"Grade 1-2\" = c(\"1\", \"2\"), \"Grade 3-4\" = c(\"3\", \"4\") ), list(\"1\" = 10, \"2\" = 5, \"3\" = 0) ) #> $`Any Grade` #> [1] 15 #> #> $`Grade 1-2` #> [1] 15 #> #> $`1` #> [1] 10 #> #> $`2` #> [1] 5 #> #> $`Grade 3-4` #> [1] 0 #> #> $`3` #> [1] 0 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Obtain column indices — h_col_indices","title":"Obtain column indices — h_col_indices","text":"Helper function extract column indices VTableTree given vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Obtain column indices — h_col_indices","text":"","code":"h_col_indices(table_tree, col_names)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Obtain column indices — h_col_indices","text":"table_tree (VTableTree)rtables table object extract indices . col_names (character) vector column names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_col_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Obtain column indices — h_col_indices","text":"vector column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for s_count_cumulative() — h_count_cumulative","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"Helper function calculate count fraction x values lower upper tail given threshold.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"","code":"h_count_cumulative( x, threshold, lower_tail = TRUE, include_eq = TRUE, na.rm = TRUE, .N_col )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"x (numeric) vector numbers want analyze. threshold (numeric(1)) cutoff value threshold count values x. lower_tail (flag) whether count lower tail, default TRUE. include_eq (flag) whether include value equal threshold count, default TRUE. na.rm (flag) whether NA values removed x prior analysis. .N_col (integer(1)) column-wise N (column count) full column analyzed typically passed rtables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"named vector items: count: count values less , less equal , greater , greater equal threshold user specification. fraction: fraction count.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_count_cumulative.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for s_count_cumulative() — h_count_cumulative","text":"","code":"set.seed(1, kind = \"Mersenne-Twister\") x <- c(sample(1:10, 10), NA) .N_col <- length(x) h_count_cumulative(x, 5, .N_col = .N_col) #> count fraction #> 5.0000000 0.4545455 h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col) #> count fraction #> 6.0000000 0.5454545 h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col) #> count fraction #> 10.0000000 0.9090909 h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col) #> count fraction #> 0 0"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for Cox proportional hazards regression — h_cox_regression","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"Helper functions used fit_coxreg_univar() fit_coxreg_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"","code":"h_coxreg_univar_formulas(variables, interaction = FALSE) h_coxreg_multivar_formula(variables) h_coxreg_univar_extract(effect, covar, data, mod, control = control_coxreg()) h_coxreg_multivar_extract(var, data, mod, control = control_coxreg())"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"variables (named list string) list additional analysis variables. interaction (flag) TRUE, model includes interaction studied treatment candidate covariate. Note univariate models without treatment arm, multivariate models, interaction can used needs FALSE. effect (string) treatment variable. covar (string) name covariate model. data (data.frame) dataset containing variables summarize. mod (coxph) Cox regression model fitted survival::coxph(). control (list) list controls returned control_coxreg(). var (string) single variable name passed rtables requested statistics function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"h_coxreg_univar_formulas() returns character vector coercible formulas (e.g stats::.formula()). h_coxreg_multivar_formula() returns string coercible formula (e.g stats::.formula()). h_coxreg_univar_extract() returns data.frame variables effect, term, term_label, level, n, hr, lcl, ucl, pval. h_coxreg_multivar_extract() returns data.frame variables pval, hr, lcl, ucl, level, n, term, term_label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"h_coxreg_univar_formulas(): Helper Cox regression formula. Creates list formulas. used internally fit_coxreg_univar() comparison univariate Cox regression models. h_coxreg_multivar_formula(): Helper multivariate Cox regression formula. Creates formulas string. used internally fit_coxreg_multivar() comparison multivariate Cox regression models. Interactions included multivariate Cox regression model. h_coxreg_univar_extract(): Utility function help tabulate result univariate Cox regression model. h_coxreg_multivar_extract(): Tabulation multivariate Cox regressions. Utility function help tabulate result multivariate Cox regression model treatment/covariate variable.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_cox_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for Cox proportional hazards regression — h_cox_regression","text":"","code":"# `h_coxreg_univar_formulas` ## Simple formulas. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\") ) ) #> ref #> \"survival::Surv(time, status) ~ armcd\" #> X #> \"survival::Surv(time, status) ~ armcd + X\" #> y #> \"survival::Surv(time, status) ~ armcd + y\" ## Addition of an optional strata. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"), strata = \"SITE\" ) ) #> ref #> \"survival::Surv(time, status) ~ armcd + strata(SITE)\" #> X #> \"survival::Surv(time, status) ~ armcd + X + strata(SITE)\" #> y #> \"survival::Surv(time, status) ~ armcd + y + strata(SITE)\" ## Inclusion of the interaction term. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", arm = \"armcd\", covariates = c(\"X\", \"y\"), strata = \"SITE\" ), interaction = TRUE ) #> ref #> \"survival::Surv(time, status) ~ armcd + strata(SITE)\" #> X #> \"survival::Surv(time, status) ~ armcd * X + strata(SITE)\" #> y #> \"survival::Surv(time, status) ~ armcd * y + strata(SITE)\" ## Only covariates fitted in separate models. h_coxreg_univar_formulas( variables = list( time = \"time\", event = \"status\", covariates = c(\"X\", \"y\") ) ) #> X y #> \"survival::Surv(time, status) ~ 1 + X\" \"survival::Surv(time, status) ~ 1 + y\" # `h_coxreg_multivar_formula` h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\") ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE\" # Addition of an optional strata. h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", arm = \"ARMCD\", covariates = c(\"RACE\", \"AGE\"), strata = \"SITE\" ) ) #> [1] \"survival::Surv(AVAL, event) ~ ARMCD + RACE + AGE + strata(SITE)\" # Example without treatment arm. h_coxreg_multivar_formula( variables = list( time = \"AVAL\", event = \"event\", covariates = c(\"RACE\", \"AGE\"), strata = \"SITE\" ) ) #> [1] \"survival::Surv(AVAL, event) ~ 1 + RACE + AGE + strata(SITE)\" library(survival) dta_simple <- data.frame( time = c(5, 5, 10, 10, 5, 5, 10, 10), status = c(0, 0, 1, 0, 0, 1, 1, 1), armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c(\"A\", \"B\")), var1 = c(45, 55, 65, 75, 55, 65, 85, 75), var2 = c(\"F\", \"M\", \"F\", \"M\", \"F\", \"M\", \"F\", \"U\") ) mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_univar_extract( effect = \"armcd\", covar = \"armcd\", mod = mod, data = dta_simple ) result #> effect term term_label level n hr lcl ucl #> 1 Treatment: armcd B vs control (A) B 8 6.551448 0.4606904 93.16769 #> pval #> 1 0.165209 mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple) result <- h_coxreg_multivar_extract( var = \"var1\", mod = mod, data = dta_simple ) result #> pval hr lcl ucl level n term term_label #> 2 0.4456195 0.9423284 0.808931 1.097724 var1 8 var1 var1"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to tidy survival fit data — h_data_plot","title":"Helper function to tidy survival fit data — h_data_plot","text":"Convert survival fit data data frame designed plotting within g_km. starts broom::tidy() result, : Post-processes strata column factor. Extends stratum additional first row time 0 probability 1 downstream plot lines start coordinates. Adds censor column. Filters rows max_time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to tidy survival fit data — h_data_plot","text":"","code":"h_data_plot(fit_km, armval = \"All\", max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to tidy survival fit data — h_data_plot","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to tidy survival fit data — h_data_plot","text":"tibble columns time, n.risk, n.event, n.censor, estimate, std.error, conf.high, conf.low, strata, censor.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_data_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to tidy survival fit data — h_data_plot","text":"","code":"library(dplyr) library(survival) # Test with multiple arms tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot() #> # A tibble: 203 × 10 #> time n.risk n.event n.censor estimate std.error conf.high conf.low strata #> #> 1 0 70 0 0 1 0 1 1 ARM A #> 2 10.4 69 1 0 0.986 0.0146 1 0.958 ARM A #> 3 20.5 68 1 0 0.971 0.0208 1 0.932 ARM A #> 4 21.5 67 1 0 0.957 0.0257 1 0.910 ARM A #> 5 25.3 66 0 1 0.957 0.0257 1 0.910 ARM A #> 6 48.1 65 1 0 0.942 0.0300 0.999 0.888 ARM A #> 7 66.6 64 0 1 0.942 0.0300 0.999 0.888 ARM A #> 8 73.9 63 0 1 0.942 0.0300 0.999 0.888 ARM A #> 9 78.9 62 0 1 0.942 0.0300 0.999 0.888 ARM A #> 10 90.3 61 1 0 0.926 0.0342 0.991 0.866 ARM A #> # ℹ 193 more rows #> # ℹ 1 more variable: censor # Test with single arm tern_ex_adtte %>% filter(PARAMCD == \"OS\", ARMCD == \"ARM B\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot(armval = \"ARM B\") #> # A tibble: 74 × 10 #> time n.risk n.event n.censor estimate std.error conf.high conf.low strata #> #> 1 0 74 0 0 1 0 1 1 ARM B #> 2 2.17 73 1 0 0.986 0.0138 1 0.960 ARM B #> 3 16.9 72 1 0 0.973 0.0196 1 0.936 ARM B #> 4 22.6 71 1 0 0.959 0.0242 1 0.914 ARM B #> 5 26.3 70 1 0 0.945 0.0282 0.999 0.894 ARM B #> 6 57.5 69 1 0 0.932 0.0317 0.991 0.875 ARM B #> 7 80.0 68 1 0 0.918 0.0350 0.983 0.857 ARM B #> 8 81.6 67 1 0 0.904 0.0381 0.974 0.839 ARM B #> 9 88.6 66 1 0 0.890 0.0411 0.965 0.822 ARM B #> 10 102. 65 1 0 0.877 0.0439 0.955 0.804 ARM B #> # ℹ 64 more rows #> # ℹ 1 more variable: censor "},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":null,"dir":"Reference","previous_headings":"","what":"ggplot decomposition — h_decompose_gg","title":"ggplot decomposition — h_decompose_gg","text":"elements composing ggplot extracted organized list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggplot decomposition — h_decompose_gg","text":"","code":"h_decompose_gg(gg)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggplot decomposition — h_decompose_gg","text":"gg (ggplot) graphic decompose.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggplot decomposition — h_decompose_gg","text":"named list elements: panel: panel. yaxis: y-axis. xaxis: x-axis. xlab: x-axis label. ylab: y-axis label. guide: legend.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_decompose_gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggplot decomposition — h_decompose_gg","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, yval = \"Survival\", censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\" ) #> Warning: `h_ggkm()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. g_el <- h_decompose_gg(gg) #> Warning: `h_decompose_gg()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"red\", fill = \"gray85\", lwd = 5)) grid::grid.draw(g_el$panel) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"royalblue\", fill = \"gray85\", lwd = 5)) grid::grid.draw(with(g_el, cbind(ylab, yaxis))) # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"","code":"h_format_row(x, format, labels = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"x (named list) list numerical values formatted optionally labeled. Elements x must numeric vectors. format (named character NULL) format patterns x. Names format must match names x. parameter passed directly rtables::format_rcell function format parameter. labels (named character NULL) optional labels x. Names labels must match names x. label specified element x, function tries use label names (order) attribute element (depending one exists NULL NA NaN). none attributes attached given element x, label automatically generated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"single row data.frame object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_format_row.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to format the optional g_lineplot table — h_format_row","text":"","code":"mean_ci <- c(48, 51) x <- list(mean = 50, mean_ci = mean_ci) format <- c(mean = \"xx.x\", mean_ci = \"(xx.xx, xx.xx)\") labels <- c(mean = \"My Mean\") h_format_row(x, format, labels) #> My Mean V1 #> 1 50.0 (48.00, 51.00) attr(mean_ci, \"label\") <- \"Mean 95% CI\" x <- list(mean = 50, mean_ci = mean_ci) h_format_row(x, format, labels) #> My Mean Mean 95% CI #> 1 50.0 (48.00, 51.00)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create simple line plot over time — h_g_ipp","title":"Helper function to create simple line plot over time — h_g_ipp","text":"Function generates simple line plot displaying parameter trends time.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create simple line plot over time — h_g_ipp","text":"","code":"h_g_ipp( df, xvar, yvar, xlab, ylab, id_var, title = \"Individual Patient Plots\", subtitle = \"\", caption = NULL, add_baseline_hline = FALSE, yvar_baseline = \"BASE\", ggtheme = nestcolor::theme_nest(), col = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create simple line plot over time — h_g_ipp","text":"df (data.frame) data set containing analysis variables. xvar (string) time point variable plotted x-axis. yvar (string) continuous analysis variable plotted y-axis. xlab (string) plot label x-axis. ylab (string) plot label y-axis. id_var (string) variable used patient identifier. title (string) title plot. subtitle (string) subtitle plot. caption (string) optional caption plot. add_baseline_hline (flag) adds horizontal line baseline y-value plot TRUE. yvar_baseline (string) variable baseline values . Ignored add_baseline_hline FALSE. ggtheme (theme) optional graphical theme function provided ggplot2 control outlook plot. Use ggplot2::theme() tweak display. col (character) line colors.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create simple line plot over time — h_g_ipp","text":"ggplot line plot.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_g_ipp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create simple line plot over time — h_g_ipp","text":"","code":"library(dplyr) library(nestcolor) # Select a small sample of data to plot. adlb <- tern_ex_adlb %>% filter(PARAMCD == \"ALT\", !(AVISIT %in% c(\"SCREENING\", \"BASELINE\"))) %>% slice(1:36) p <- h_g_ipp( df = adlb, xvar = \"AVISIT\", yvar = \"AVAL\", xlab = \"Visit\", id_var = \"USUBJID\", ylab = \"SGOT/ALT (U/L)\", add_baseline_hline = TRUE ) p"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a KM plot — h_ggkm","title":"Helper function to create a KM plot — h_ggkm","text":"Draw Kaplan-Meier plot using ggplot2.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a KM plot — h_ggkm","text":"","code":"h_ggkm( data, xticks = NULL, yval = \"Survival\", censor_show, xlab, ylab, ylim = NULL, title, footnotes = NULL, max_time = NULL, lwd = 1, lty = NULL, pch = 3, size = 2, col = NULL, ci_ribbon = FALSE, ggtheme = nestcolor::theme_nest() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a KM plot — h_ggkm","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. yval (string) type plot, plotted y-axis. Options Survival (default) Failure probability. censor_show (flag) whether show censored observations. xlab (string) x-axis label. ylab (string) y-axis label. ylim (numeric(2)) vector containing lower upper limits y-axis, respectively. NULL (default), default scale range used. title (string) plot title. footnotes (string) plot footnotes. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL). lwd (numeric) line width. vector given, length equal number strata survival::survfit(). lty (numeric) line type. vector given, length equal number strata survival::survfit(). pch (string) name symbol character use point symbol indicate censored cases. size (numeric(1)) size censored point symbols. col (character) lines colors. Length vector equal number strata survival::survfit(). ci_ribbon (flag) whether confidence interval drawn around Kaplan-Meier curve. ggtheme (theme) graphical theme provided ggplot2 format Kaplan-Meier plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a KM plot — h_ggkm","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ggkm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a KM plot — h_ggkm","text":"","code":"# \\donttest{ library(dplyr) library(survival) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", yval = \"Survival\", ylab = \"Survival Probability\", title = \"Survival\" ) gg # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for Poisson models — h_glm_count","title":"Helper functions for Poisson models — h_glm_count","text":"Helper functions returns results stats::glm() Poisson Quasi-Poisson distributions needed (see family parameter), MASS::glm.nb() Negative Binomial distributions. Link function GLM log.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for Poisson models — h_glm_count","text":"","code":"h_glm_count(.var, .df_row, variables, distribution, weights) h_glm_poisson(.var, .df_row, variables, weights) h_glm_quasipoisson(.var, .df_row, variables, weights) h_glm_negbin(.var, .df_row, variables, weights)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for Poisson models — h_glm_count","text":".var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) dataset includes variables called .var variables. variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". offset (numeric) numeric vector scalar adding offset. distribution (character) character value specifying distribution used regression (Poisson, Quasi-Poisson, negative binomial). weights (character) character vector specifying weights used averaging predictions. Number weights must equal number levels included covariates. Weights option passed emmeans::emmeans().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for Poisson models — h_glm_count","text":"h_glm_count() returns results selected model. h_glm_poisson() returns results Poisson model. h_glm_quasipoisson() returns results Quasi-Poisson model. h_glm_negbin() returns results negative binomial model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_glm_count.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for Poisson models — h_glm_count","text":"h_glm_count(): Helper function return results selected model (Poisson, Quasi-Poisson, negative binomial). h_glm_poisson(): Helper function return results Poisson model. h_glm_quasipoisson(): Helper function return results Quasi-Poisson model. h_glm_negbin(): Helper function return results negative binomial model.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create Cox-PH grobs — h_grob_coxph","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"Grob rtable output h_tbl_coxph_pairwise()","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"","code":"h_grob_coxph( ..., x = 0, y = 0, width = grid::unit(0.4, \"npc\"), ttheme = gridExtra::ttheme_default(padding = grid::unit(c(1, 0.5), \"lines\"), core = list(bg_params = list(fill = c(\"grey95\", \"grey90\"), alpha = 0.5))) )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"... arguments pass h_tbl_coxph_pairwise(). x (proportion) value 0 1 specifying x-location. y (proportion) value 0 1 specifying y-location. width (grid::unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"grob table containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create Cox-PH grobs — h_grob_coxph","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) data <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) tbl_grob <- h_grob_coxph( df = data, variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARMCD\"), control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5 ) #> Warning: `h_grob_coxph()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.draw(tbl_grob) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create survival estimation grobs — h_grob_median_surv","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"survival fit transformed grob containing table groups rows characterized N, median 95% confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"","code":"h_grob_median_surv( fit_km, armval = \"All\", x = 0.9, y = 0.9, width = grid::unit(0.3, \"npc\"), ttheme = gridExtra::ttheme_default() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\". x (proportion) value 0 1 specifying x-location. y (proportion) value 0 1 specifying y-location. width (grid::unit) width (unit) use printing grob. ttheme (list) see gridExtra::ttheme_default().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"grob table containing statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create survival estimation grobs — h_grob_median_surv","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) grid::grid.newpage() grid.rect(gp = grid::gpar(lty = 1, col = \"pink\", fill = \"gray85\", lwd = 1)) tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_grob_median_surv() %>% grid::grid.draw() #> Warning: `h_grob_median_surv()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"Two graphical objects obtained, one corresponding row labeling second table numbers patients risk. title = TRUE, third object corresponding table title also obtained.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"","code":"h_grob_tbl_at_risk(data, annot_tbl, xlim, title = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"data (data.frame) survival data pre-processed h_data_plot. annot_tbl (data.frame) annotation prepared survival::summary.survfit() includes number patients risk given time points. xlim (numeric(1)) maximum value x-axis (used ensure risk table aligns KM graph). title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"named list two gTree objects title = FALSE: at_risk label, three gTree objects title = TRUE: at_risk, label, title.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_tbl_at_risk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create patient-at-risk grobs — h_grob_tbl_at_risk","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) # The annotation table reports the patient at risk for a given strata and # times (`xticks`). annot_tbl <- summary(fit_km, times = xticks) if (is.null(fit_km$strata)) { annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = \"All\")) } else { strata_lst <- strsplit(sub(\"=\", \"equals\", levels(annot_tbl$strata)), \"equals\") levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2] annot_tbl <- data.frame( n.risk = annot_tbl$n.risk, time = annot_tbl$time, strata = annot_tbl$strata ) } # The annotation table is transformed into a grob. tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks)) #> Warning: `h_grob_tbl_at_risk()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. # For the representation, the layout is estimated for which the decomposition # of the graphic element is necessary. g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") #> Warning: `h_km_layout()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid::grid.newpage() pushViewport(viewport(layout = lyt, height = .95, width = .95)) grid.rect(gp = grid::gpar(lty = 1, col = \"purple\", fill = \"gray85\", lwd = 1)) pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2)) grid.rect(gp = grid::gpar(lty = 1, col = \"orange\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$at_risk) popViewport() pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1)) grid.rect(gp = grid::gpar(lty = 1, col = \"green3\", fill = \"gray85\", lwd = 1)) grid::grid.draw(tbl$label) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"Build y-axis annotation decomposed ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"","code":"h_grob_y_annot(ylab, yaxis)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"ylab (gtable) y-lab graphical object derived ggplot. yaxis (gtable) y-axis graphical object derived ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"gTree object containing y-axis annotation ggplot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_grob_y_annot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create grid object with y-axis annotation — h_grob_y_annot","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"title\", footnotes = \"footnotes\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) grid::grid.newpage() pvp <- grid::plotViewport(margins = c(5, 4, 2, 20)) pushViewport(pvp) grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis)) #> Warning: `h_grob_y_annot()` was deprecated in tern 0.9.4. #> ℹ `g_km` now generates `ggplot` objects. This function is no longer used within #> `tern`. grid.rect(gp = grid::gpar(lty = 1, col = \"gray35\", fill = NA)) # }"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for incidence rate — h_incidence_rate","text":"","code":"h_incidence_rate(person_years, n_events, control = control_incidence_rate()) h_incidence_rate_normal(person_years, n_events, alpha = 0.05) h_incidence_rate_normal_log(person_years, n_events, alpha = 0.05) h_incidence_rate_exact(person_years, n_events, alpha = 0.05) h_incidence_rate_byar(person_years, n_events, alpha = 0.05)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for incidence rate — h_incidence_rate","text":"person_years (numeric(1)) total person-years risk. n_events (integer(1)) number events observed. control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level: (proportion) confidence level estimated incidence rate. conf_type: (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit: (string)day, week, month, year (default) indicating time unit data input. num_pt_year: (numeric) time unit desired output (person-years). alpha (numeric(1)) two-sided alpha-level confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for incidence rate — h_incidence_rate","text":"Estimated incidence rate, rate, associated confidence interval, rate_ci.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for incidence rate — h_incidence_rate","text":"h_incidence_rate(): Helper function estimate incidence rate associated confidence interval. h_incidence_rate_normal(): Helper function estimate incidence rate associated confidence interval based normal approximation incidence rate. Unit one person-year. h_incidence_rate_normal_log(): Helper function estimate incidence rate associated confidence interval based normal approximation logarithm incidence rate. Unit one person-year. h_incidence_rate_exact(): Helper function estimate incidence rate associated exact confidence interval. Unit one person-year. h_incidence_rate_byar(): Helper function estimate incidence rate associated Byar's confidence interval. Unit one person-year.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for incidence rate — h_incidence_rate","text":"","code":"h_incidence_rate_normal(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] -0.003859038 0.023859038 #> h_incidence_rate_normal_log(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.002500977 0.039984382 #> h_incidence_rate_exact(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.001211046 0.036123438 #> h_incidence_rate_byar(200, 2) #> $rate #> [1] 0.01 #> #> $rate_ci #> [1] 0.001994207 0.032054171 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to prepare a KM layout — h_km_layout","title":"Helper function to prepare a KM layout — h_km_layout","text":"Prepares (5 rows) x (2 cols) layout Kaplan-Meier curve.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to prepare a KM layout — h_km_layout","text":"","code":"h_km_layout( data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to prepare a KM layout — h_km_layout","text":"data (data.frame) survival data pre-processed h_data_plot. g_el (list gtable) list obtained h_decompose_gg(). title (string) plot title. footnotes (string) plot footnotes. annot_at_risk (flag) compute add annotation table reporting number patient risk matching main grid Kaplan-Meier curve. annot_at_risk_title (flag) whether \"Patients Risk\" title added annot_at_risk table. effect annot_at_risk FALSE. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to prepare a KM layout — h_km_layout","text":"grid layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper function to prepare a KM layout — h_km_layout","text":"layout corresponds grid two columns five rows unequal dimensions. dimension fixed, curve flexible accommodate remaining free space. left column gets annotation ggplot (y-axis) names strata patient risk tabulation. main constraint width columns must allow writing strata name. right column receive ggplot, legend, x-axis patient risk table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_km_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to prepare a KM layout — h_km_layout","text":"","code":"# \\donttest{ library(dplyr) library(survival) library(grid) fit_km <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) data_plot <- h_data_plot(fit_km = fit_km) xticks <- h_xticks(data = data_plot) gg <- h_ggkm( data = data_plot, censor_show = TRUE, xticks = xticks, xlab = \"Days\", ylab = \"Survival Probability\", title = \"tt\", footnotes = \"ff\", yval = \"Survival\" ) g_el <- h_decompose_gg(gg) lyt <- h_km_layout(data = data_plot, g_el = g_el, title = \"t\", footnotes = \"f\") grid.show.layout(lyt) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for multivariate logistic regression — h_logistic_regression","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"Helper functions used calculations logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"","code":"h_get_interaction_vars(fit_glm) h_interaction_coef_name( interaction_vars, first_var_with_level, second_var_with_level ) h_or_cat_interaction( odds_ratio_var, interaction_var, fit_glm, conf_level = 0.95 ) h_or_cont_interaction( odds_ratio_var, interaction_var, fit_glm, at = NULL, conf_level = 0.95 ) h_or_interaction( odds_ratio_var, interaction_var, fit_glm, at = NULL, conf_level = 0.95 ) h_simple_term_labels(terms, table) h_interaction_term_labels(terms1, terms2, table, any = FALSE) h_glm_simple_term_extract(x, fit_glm) h_glm_interaction_extract(x, fit_glm) h_glm_inter_term_extract(odds_ratio_var, interaction_var, fit_glm, ...) h_logistic_simple_terms(x, fit_glm, conf_level = 0.95) h_logistic_inter_terms(x, fit_glm, conf_level = 0.95, at = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"fit_glm (glm) logistic regression model fitted stats::glm() \"binomial\" family. Limited functionality also available conditional logistic regression models fitted survival::clogit(), currently used extract_rsp_biomarkers(). interaction_vars (character(2)) interaction variable names. first_var_with_level (character(2)) first variable name interaction level. second_var_with_level (character(2)) second variable name interaction level. odds_ratio_var (string) odds ratio variable. interaction_var (string) interaction variable. conf_level (proportion) confidence level interval. (numeric NULL) optional values interaction variable. Otherwise median used. terms (character) simple terms. table (table) table containing numbers terms. terms1 (character) terms first dimension (rows). terms2 (character) terms second dimension (rows). (flag) whether term1 term2 can fulfilled count number patients. case can scalar (strings). x (character) variable interaction term fit_glm (depending helper function used). ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"Vector names interaction variables. Name coefficient. Odds ratio. Odds ratio. Odds ratio. Term labels containing numbers patients. Term labels containing numbers patients. Tabulated main effect results logistic regression model. Tabulated interaction term results logistic regression model. data.frame tabulated interaction term results logistic regression model. Tabulated statistics given variable(s) logistic regression model. Tabulated statistics given variable(s) logistic regression model.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"h_get_interaction_vars(): Helper function extract interaction variable names fitted model assuming one interaction term. h_interaction_coef_name(): Helper function get right coefficient name interaction variable names given levels. main value order first second variable checked interaction_vars input. h_or_cat_interaction(): Helper function calculate odds ratio estimates case odds ratio interaction variable categorical. h_or_cont_interaction(): Helper function calculate odds ratio estimates case either odds ratio interaction variable continuous. h_or_interaction(): Helper function calculate odds ratio estimates case interaction. wrapper h_or_cont_interaction() h_or_cat_interaction(). h_simple_term_labels(): Helper function construct term labels simple terms table numbers patients. h_interaction_term_labels(): Helper function construct term labels interaction terms table numbers patients. h_glm_simple_term_extract(): Helper function tabulate main effect results (conditional) logistic regression model. h_glm_interaction_extract(): Helper function tabulate interaction term results logistic regression model. h_glm_inter_term_extract(): Helper function tabulate interaction results logistic regression model. basically wrapper h_or_interaction() h_glm_simple_term_extract() puts results right data frame format. h_logistic_simple_terms(): Helper function tabulate results including odds ratios confidence intervals simple terms. h_logistic_inter_terms(): Helper function tabulate results including odds ratios confidence intervals interaction terms.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"provide function case variables continuous arise table, treatment arm variable always involved categorical.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_logistic_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for multivariate logistic regression — h_logistic_regression","text":"","code":"library(dplyr) library(broom) adrs_f <- tern_ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(RACE %in% c(\"ASIAN\", \"WHITE\", \"BLACK OR AFRICAN AMERICAN\")) %>% mutate( Response = case_when(AVALC %in% c(\"PR\", \"CR\") ~ 1, TRUE ~ 0), RACE = factor(RACE), SEX = factor(SEX) ) formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = \"Response\") mod1 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\") ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred mod2 <- fit_logistic( data = adrs_f, variables = list( response = \"Response\", arm = \"ARMCD\", covariates = c(\"AGE\", \"RACE\"), interaction = \"AGE\" ) ) #> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred h_glm_simple_term_extract(\"AGE\", mod1) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> reference reference_label estimate std_error df pvalue #> 1 0.1698216 0.09524116 1 0.07457501 #> is_variable_summary is_term_summary #> 1 FALSE TRUE h_glm_simple_term_extract(\"ARMCD\", mod1) #> variable variable_label term term_label interaction #> 1 ARMCD Planned Arm Code ARM A Reference ARM A, n = 64 #> 2 ARMCD Planned Arm Code ARM B ARM B, n = 68 #> 3 ARMCD Planned Arm Code ARM C ARM C, n = 52 #> interaction_label reference reference_label estimate std_error df pvalue #> 1 2 0.3004308 #> 2 -1.774769 1.144405 1 0.1209443 #> 3 17.1922 3626.588 1 0.9962176 #> is_variable_summary is_term_summary #> 1 TRUE FALSE #> 2 FALSE TRUE #> 3 FALSE TRUE h_glm_interaction_extract(\"ARMCD:AGE\", mod2) #> variable variable_label term term_label #> 1 ARMCD:AGE Interaction of Planned Arm Code * Age ARM A Reference ARM A, n = 64 #> 2 ARMCD:AGE Interaction of Planned Arm Code * Age ARM B ARM B, n = 68 #> 3 ARMCD:AGE Interaction of Planned Arm Code * Age ARM C ARM C, n = 52 #> interaction interaction_label reference reference_label estimate std_error #> 1 #> 2 0.3081205 0.2062392 #> 3 0.02948826 548.5923 #> df pvalue is_variable_summary is_term_summary #> 1 2 0.3275837 TRUE FALSE #> 2 1 0.1351767 FALSE TRUE #> 3 1 0.9999571 FALSE TRUE h_glm_inter_term_extract(\"AGE\", \"ARMCD\", mod2) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> 2 AGE Age AGE Age ARMCD Planned Arm Code #> 3 AGE Age AGE Age ARMCD Planned Arm Code #> 4 AGE Age AGE Age ARMCD Planned Arm Code #> reference reference_label estimate std_error odds_ratio lcl ucl #> 1 -0.03873898 0.1514322 NA NA NA #> 2 ARM A ARM A NA NA 0.9620018 0.7149514 1.294420 #> 3 ARM B ARM B NA NA 1.3091545 1.0021802 1.710157 #> 4 ARM C ARM C NA NA 0.9907919 0.0000000 Inf #> df pvalue is_variable_summary is_term_summary is_reference_summary #> 1 1 0.798092 FALSE TRUE FALSE #> 2 NA NA FALSE FALSE TRUE #> 3 NA NA FALSE FALSE TRUE #> 4 NA NA FALSE FALSE TRUE h_logistic_simple_terms(\"AGE\", mod1) #> variable variable_label term term_label interaction interaction_label #> 1 AGE Age AGE Age #> reference reference_label estimate std_error df pvalue #> 1 0.1698216 0.09524116 1 0.07457501 #> is_variable_summary is_term_summary odds_ratio lcl ucl #> 1 FALSE TRUE 1.185093 0.9832935 1.428308 #> ci #> 1 0.9832935, 1.4283084 h_logistic_inter_terms(c(\"RACE\", \"AGE\", \"ARMCD\", \"AGE:ARMCD\"), mod2) #> variable variable_label term #> 1 RACE Race ASIAN #> 2 RACE Race BLACK OR AFRICAN AMERICAN #> 3 RACE Race WHITE #> 13 ARMCD Planned Arm Code ARM A #> 23 ARMCD Planned Arm Code ARM B #> ARM B ARMCD Planned Arm Code ARM B #> 33 ARMCD Planned Arm Code ARM C #> ARM C ARMCD Planned Arm Code ARM C #> 11 AGE Age AGE #> 21 AGE Age AGE #> 31 AGE Age AGE #> 4 AGE Age AGE #> 12 AGE:ARMCD Interaction of Planned Arm Code * Age ARM A #> 22 AGE:ARMCD Interaction of Planned Arm Code * Age ARM B #> 32 AGE:ARMCD Interaction of Planned Arm Code * Age ARM C #> term_label interaction interaction_label reference #> 1 Reference ASIAN, n = 110 #> 2 BLACK OR AFRICAN AMERICAN, n = 40 #> 3 WHITE, n = 34 #> 13 Reference ARM A, n = 64 #> 23 ARM B, n = 68 #> ARM B ARM B, n = 68 AGE Age 35 #> 33 ARM C, n = 52 #> ARM C ARM C, n = 52 AGE Age 35 #> 11 Age #> 21 Age ARMCD Planned Arm Code ARM A #> 31 Age ARMCD Planned Arm Code ARM B #> 4 Age ARMCD Planned Arm Code ARM C #> 12 Reference ARM A, n = 64 #> 22 ARM B, n = 68 #> 32 ARM C, n = 52 #> reference_label estimate std_error df pvalue odds_ratio lcl #> 1 2 0.9361139 #> 2 18.15018 3944.701 1 0.9963288 76299564 0 #> 3 -0.3727152 1.025808 1 0.7163522 0.6888614 0.09224926 #> 13 2 0.253914 NA NA #> 23 -11.52715 6.96188 1 0.09777185 NA NA #> ARM B 35 NA NA NA NA 0.4757148 0.03361147 #> 33 16.33327 20278.39 1 0.9993573 NA NA #> ARM C 35 NA NA NA NA 34808099 0 #> 11 -0.03873898 0.1514322 1 0.798092 NA NA #> 21 ARM A NA NA NA NA 0.9620018 0.7149514 #> 31 ARM B NA NA NA NA 1.309155 1.00218 #> 4 ARM C NA NA NA NA 0.9907919 0 #> 12 NA NA NA NA #> 22 0.3081205 0.2062392 1 0.1351767 NA NA #> 32 0.02948826 548.5923 1 0.9999571 NA NA #> ucl is_variable_summary is_term_summary is_reference_summary #> 1 TRUE FALSE FALSE #> 2 Inf FALSE TRUE FALSE #> 3 5.143998 FALSE TRUE FALSE #> 13 NA TRUE FALSE FALSE #> 23 NA FALSE TRUE FALSE #> ARM B 6.732956 FALSE FALSE TRUE #> 33 NA FALSE TRUE FALSE #> ARM C Inf FALSE FALSE TRUE #> 11 NA FALSE TRUE FALSE #> 21 1.29442 FALSE FALSE TRUE #> 31 1.710157 FALSE FALSE TRUE #> 4 Inf FALSE FALSE TRUE #> 12 NA TRUE FALSE FALSE #> 22 NA FALSE TRUE FALSE #> 32 NA FALSE TRUE FALSE #> ci #> 1 #> 2 0, Inf #> 3 0.09224926, 5.14399810 #> 13 NA, NA #> 23 NA, NA #> ARM B 0.03361147, 6.73295611 #> 33 NA, NA #> ARM C 0, Inf #> 11 NA, NA #> 21 0.7149514, 1.2944200 #> 31 1.002180, 1.710157 #> 4 0, Inf #> 12 NA, NA #> 22 NA, NA #> 32 NA, NA"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"Helper function create map data frame input dataset, can used argument trim_levels_to_map split function. Based different method, map constructed differently.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"","code":"h_map_for_count_abnormal( df, variables = list(anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\", range_high = \"ANRHI\"), abnormal = list(low = c(\"LOW\", \"LOW LOW\"), high = c(\"HIGH\", \"HIGH HIGH\")), method = c(\"default\", \"range\"), na_str = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"df (data.frame) data set containing analysis variables. variables (named list string) list additional analysis variables. abnormal (named list) identifying abnormal range level(s) df. Based levels abnormality input dataset, can something like list(Low = \"LOW LOW\", High = \"HIGH HIGH\") abnormal = list(Low = \"LOW\", High = \"HIGH\")) method (string) indicates returned map constructed. Can \"default\" \"range\". na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"map data.frame.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"method \"default\", returned map abnormal directions observed df, records normal values excluded avoid error creating layout. method \"range\", returned map based rule least one observation low range > 0 low direction least one observation high range missing high direction.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_map_for_count_abnormal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a map data frame for trim_levels_to_map() — h_map_for_count_abnormal","text":"","code":"adlb <- df_explicit_na(tern_ex_adlb) h_map_for_count_abnormal( df = adlb, variables = list(anl = \"ANRIND\", split_rows = c(\"LBCAT\", \"PARAM\")), abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")), method = \"default\", na_str = \"\" ) #> LBCAT PARAM ANRIND #> 1 CHEMISTRY Alanine Aminotransferase Measurement LOW #> 4 CHEMISTRY Alanine Aminotransferase Measurement HIGH #> 7 CHEMISTRY Alanine Aminotransferase Measurement NORMAL #> 2 CHEMISTRY C-Reactive Protein Measurement LOW #> 3 CHEMISTRY C-Reactive Protein Measurement HIGH #> 8 CHEMISTRY C-Reactive Protein Measurement NORMAL #> 5 IMMUNOLOGY Immunoglobulin A Measurement LOW #> 6 IMMUNOLOGY Immunoglobulin A Measurement HIGH #> 9 IMMUNOLOGY Immunoglobulin A Measurement NORMAL df <- data.frame( USUBJID = c(rep(\"1\", 4), rep(\"2\", 4), rep(\"3\", 4)), AVISIT = c( rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2), rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2), rep(\"WEEK 1\", 2), rep(\"WEEK 2\", 2) ), PARAM = rep(c(\"ALT\", \"CPR\"), 6), ANRIND = c( \"NORMAL\", \"NORMAL\", \"LOW\", \"HIGH\", \"LOW\", \"LOW\", \"HIGH\", \"HIGH\", rep(\"NORMAL\", 4) ), ANRLO = rep(5, 12), ANRHI = rep(20, 12) ) df$ANRIND <- factor(df$ANRIND, levels = c(\"LOW\", \"HIGH\", \"NORMAL\")) h_map_for_count_abnormal( df = df, variables = list( anl = \"ANRIND\", split_rows = c(\"PARAM\"), range_low = \"ANRLO\", range_high = \"ANRHI\" ), abnormal = list(low = c(\"LOW\"), high = c(\"HIGH\")), method = \"range\", na_str = \"\" ) #> PARAM ANRIND #> 1 ALT LOW #> 3 ALT HIGH #> 5 ALT NORMAL #> 2 CPR LOW #> 4 CPR HIGH #> 6 CPR NORMAL"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for odds ratio estimation — h_odds_ratio","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"Functions calculate odds ratios estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"","code":"or_glm(data, conf_level) or_clogit(data, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"data (data.frame) data frame containing least variables rsp grp, optionally strata or_clogit(). conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"named list elements or_ci n_tot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"or_glm(): Estimates odds ratio based stats::glm(). Note must exactly 2 groups data specified grp variable. or_clogit(): Estimates odds ratio based survival::clogit(). done whole data set including groups, since results pairwise comparisons groups.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for odds ratio estimation — h_odds_ratio","text":"","code":"# Data with 2 groups. data <- data.frame( rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)), grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)], strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)], stringsAsFactors = TRUE ) # Odds ratio based on glm. or_glm(data, conf_level = 0.95) #> $or_ci #> est lcl ucl #> 0.33333333 0.01669735 6.65441589 #> #> $n_tot #> n_tot #> 8 #> # Data with 3 groups. data <- data.frame( rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)), grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)], strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)], stringsAsFactors = TRUE ) # Odds ratio based on stratified estimation by conditional logistic regression. or_clogit(data, conf_level = 0.95) #> $or_ci #> $or_ci$b #> est lcl ucl #> 0.28814553 0.02981009 2.78522598 #> #> $or_ci$c #> est lcl ucl #> 0.5367919 0.0673365 4.2791881 #> #> #> $n_tot #> n_tot #> 20 #>"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"","code":"h_pkparam_sort(pk_data, key_var = \"PARAMCD\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"pk_data (data.frame) pharmacokinetic data frame. key_var (string) key variable used merge pk_data metadata created d_pkparam().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"pharmacokinetic data.frame sorted PARAM variable.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_pkparam_sort.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort pharmacokinetic data by PARAM variable — h_pkparam_sort","text":"","code":"library(dplyr) adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, \" (\", AVALU, \")\"))) pk_ordered_data <- h_pkparam_sort(adpp)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Function to return the estimated means using predicted probabilities — h_ppmeans","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"arm level, predicted mean rate calculated using fitted model object, newdata set result stats::model.frame, reconstructed data original data, depending object formula (coming fit). confidence interval derived using conf_level parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"","code":"h_ppmeans(obj, .df_row, arm, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"obj (glm.fit) fitted model object used derive mean rate estimates treatment arm. .df_row (data.frame) dataset includes variables called .var variables. arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. conf_level (proportion) value used derive confidence interval rate.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_ppmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Function to return the estimated means using predicted probabilities — h_ppmeans","text":"h_ppmeans() returns estimated means.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"","code":"prop_diff_wald(rsp, grp, conf_level = 0.95, correct = FALSE) prop_diff_ha(rsp, grp, conf_level) prop_diff_nc(rsp, grp, conf_level, correct = FALSE) prop_diff_cmh(rsp, grp, strata, conf_level = 0.95) prop_diff_strat_nc( rsp, grp, strata, weights_method = c(\"cmh\", \"wilson_h\"), conf_level = 0.95, correct = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"rsp (logical) vector indicating whether subject responder . grp (factor) vector assigning observations one two groups (e.g. reference treatment group). conf_level (proportion) confidence level interval. correct (flag) whether include continuity correction. information, see stats::prop.test(). strata (factor) variable one level per stratum length rsp. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"named list elements diff (proportion difference) diff_ci (proportion difference confidence interval).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"prop_diff_wald(): Wald interval follows usual textbook definition single proportion confidence interval using normal approximation. possible include continuity correction Wald's interval. prop_diff_ha(): Anderson-Hauck confidence interval. prop_diff_nc(): Newcombe confidence interval. based Wilson score confidence interval single binomial proportion. prop_diff_cmh(): Calculates weighted difference. defined difference response rates experimental treatment group control treatment group, adjusted stratification factors applying Cochran-Mantel-Haenszel (CMH) weights. CMH chi-squared test, use stats::mantelhaen.test(). prop_diff_strat_nc(): Calculates stratified Newcombe confidence interval difference response rates experimental treatment group control treatment group, adjusted stratification factors. implementation follows closely one proposed Yan Su (2010) . Weights can estimated heuristic proposed prop_strat_wilson() CMH-derived weights (see prop_diff_cmh()).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329–335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions to calculate proportion difference — h_prop_diff","text":"","code":"# Wald confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20) grp <- factor(c(rep(\"A\", 10), rep(\"B\", 10))) prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE) #> $diff #> [1] 0 #> #> $diff_ci #> [1] -0.4382613 0.4382613 #> # Anderson-Hauck confidence interval ## \"Mid\" case: 3/4 respond in group A, 1/2 respond in group B. rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE) grp <- factor(c(\"A\", \"B\", \"A\", \"B\", \"A\", \"A\"), levels = c(\"B\", \"A\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90) #> $diff #> [1] 0.25 #> #> $diff_ci #> [1] -0.9195011 1.0000000 #> ## Edge case: Same proportion of response in A and B. rsp <- c(TRUE, FALSE, TRUE, FALSE) grp <- factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6) #> $diff #> [1] 0 #> #> $diff_ci #> [1] -0.8451161 0.8451161 #> # Newcombe confidence interval set.seed(1) rsp <- c( sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE), sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE) ) grp <- factor(rep(c(\"A\", \"B\"), each = 40), levels = c(\"B\", \"A\")) table(rsp, grp) #> grp #> rsp B A #> FALSE 20 10 #> TRUE 20 30 prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9) #> $diff #> [1] 0.25 #> #> $diff_ci #> [1] 0.07193388 0.40725819 #> # Cochran-Mantel-Haenszel confidence interval set.seed(2) rsp <- sample(c(TRUE, FALSE), 100, TRUE) grp <- sample(c(\"Placebo\", \"Treatment\"), 100, TRUE) grp <- factor(grp, levels = c(\"Placebo\", \"Treatment\")) strata_data <- data.frame( \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE ) prop_diff_cmh( rsp = rsp, grp = grp, strata = interaction(strata_data), conf_level = 0.90 ) #> $prop #> Placebo Treatment #> 0.5331117 0.3954251 #> #> $prop_ci #> $prop_ci$Placebo #> [1] 0.4306536 0.6355698 #> #> $prop_ci$Treatment #> [1] 0.2890735 0.5017768 #> #> #> $diff #> [1] -0.1376866 #> #> $diff_ci #> [1] -0.285363076 0.009989872 #> #> $weights #> a.x b.x a.y b.y a.z b.z #> 0.1148388 0.2131696 0.1148388 0.2131696 0.1767914 0.1671918 #> #> $n1 #> a.x b.x a.y b.y a.z b.z #> 4 11 8 11 13 11 #> #> $n2 #> a.x b.x a.y b.y a.z b.z #> 8 9 4 9 6 6 #> # Stratified Newcombe confidence interval set.seed(2) data_set <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), 100, TRUE), \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), \"grp\" = sample(c(\"Placebo\", \"Treatment\"), 100, TRUE), stringsAsFactors = TRUE ) prop_diff_strat_nc( rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]), weights_method = \"cmh\", conf_level = 0.90 ) #> $diff #> [1] -0.05777672 #> #> $diff_ci #> lower upper #> -0.2236537 0.1119331 #> prop_diff_strat_nc( rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]), weights_method = \"wilson_h\", conf_level = 0.90 ) #> $diff #> [1] -0.07771884 #> #> $diff_ci #> lower upper #> -0.2540844 0.1027720 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions to test proportion differences — h_prop_diff_test","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"Helper functions implement various tests difference two proportions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"","code":"prop_chisq(tbl) prop_cmh(ary) prop_schouten(tbl) prop_fisher(tbl)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"tbl (matrix) matrix two groups rows binary response (TRUE/FALSE) columns. ary (array, 3 dimensions) array two groups rows, binary response (TRUE/FALSE) columns, strata third dimension.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions to test proportion differences — h_prop_diff_test","text":"prop_chisq(): Performs Chi-Squared test. Internally calls stats::prop.test(). prop_cmh(): Performs stratified Cochran-Mantel-Haenszel test. Internally calls stats::mantelhaen.test(). Note strata less two observations automatically discarded. prop_schouten(): Performs Chi-Squared test Schouten correction. prop_fisher(): Performs Fisher's exact test. Internally calls stats::fisher.test().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for calculating proportion confidence intervals — h_proportions","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Functions calculate different proportion confidence intervals use estimate_proportion().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"","code":"prop_wilson(rsp, conf_level, correct = FALSE) prop_strat_wilson( rsp, strata, weights = NULL, conf_level = 0.95, max_iterations = NULL, correct = FALSE ) prop_clopper_pearson(rsp, conf_level) prop_wald(rsp, conf_level, correct = FALSE) prop_agresti_coull(rsp, conf_level) prop_jeffreys(rsp, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"rsp (logical) vector indicating whether subject responder . conf_level (proportion) confidence level interval. correct (flag) whether apply continuity correction. strata (factor) variable one level per stratum length rsp. weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. max_iterations (count) maximum number iterations iterative procedure used find estimates optimal weights.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Confidence interval proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"prop_wilson(): Calculates Wilson interval calling stats::prop.test(). Also referred Wilson score interval. prop_strat_wilson(): Calculates stratified Wilson confidence interval unequal proportions described Yan Su (2010) prop_clopper_pearson(): Calculates Clopper-Pearson interval calling stats::binom.test(). Also referred exact method. prop_wald(): Calculates Wald interval following usual textbook definition single proportion confidence interval using normal approximation. prop_agresti_coull(): Calculates Agresti-Coull interval. Constructed (95% CI) adding two successes two failures data using Wald formula construct CI. prop_jeffreys(): Calculates Jeffreys interval, equal-tailed interval based non-informative Jeffreys prior binomial proportion.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"Yan X, Su XG (2010). “Stratified Wilson Newcombe Confidence Intervals Multiple Binomial Proportions.” Stat. Biopharm. Res., 2(3), 329–335.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_proportions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for calculating proportion confidence intervals — h_proportions","text":"","code":"rsp <- c( TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE ) prop_wilson(rsp, conf_level = 0.9) #> [1] 0.2692718 0.7307282 # Stratified Wilson confidence interval with unequal probabilities set.seed(1) rsp <- sample(c(TRUE, FALSE), 100, TRUE) strata_data <- data.frame( \"f1\" = sample(c(\"a\", \"b\"), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE ) strata <- interaction(strata_data) n_strata <- ncol(table(rsp, strata)) # Number of strata prop_strat_wilson( rsp = rsp, strata = strata, conf_level = 0.90 ) #> $conf_int #> lower upper #> 0.4072891 0.5647887 #> #> $weights #> a.x b.x a.y b.y a.z b.z #> 0.2074199 0.1776464 0.1915610 0.1604678 0.1351096 0.1277952 #> # Not automatic setting of weights prop_strat_wilson( rsp = rsp, strata = strata, weights = rep(1 / n_strata, n_strata), conf_level = 0.90 ) #> $conf_int #> lower upper #> 0.4190436 0.5789733 #> prop_clopper_pearson(rsp, conf_level = .95) #> [1] 0.3886442 0.5919637 prop_wald(rsp, conf_level = 0.95) #> [1] 0.3920214 0.5879786 prop_wald(rsp, conf_level = 0.95, correct = TRUE) #> [1] 0.3870214 0.5929786 prop_agresti_coull(rsp, conf_level = 0.95) #> [1] 0.3942193 0.5865206 prop_jeffreys(rsp, conf_level = 0.95) #> [1] 0.3934779 0.5870917"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"","code":"h_rsp_to_logistic_variables(variables, biomarker) h_logistic_mult_cont_df(variables, data, control = control_logistic()) h_tab_rsp_one_biomarker(df, vars, na_str = default_na_str(), .indent_mods = 0L)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (named list) controls response definition confidence level produced control_logistic(). df (data.frame) results single biomarker, part returned extract_rsp_biomarkers() (needs couple columns added high-level function relative returned h_logistic_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables() returns named list elements response, arm, covariates, strata. h_logistic_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_rsp_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"h_rsp_to_logistic_variables(): helps converting \"response\" function variable list \"logistic regression\" variable list. reason currently inconsistency variable names accepted extract_rsp_subgroups() fit_logistic(). h_logistic_mult_cont_df(): prepares estimates number responses, patients overall response rate, well odds ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements rsp biomarkers (vector continuous biomarker variables) optionally covariates strata. h_tab_rsp_one_biomarker(): Prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating biomarker effects on binary response by subgroup — h_response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") # This is how the variable list is converted internally. h_rsp_to_logistic_variables( variables = list( rsp = \"RSP\", covariates = c(\"A\", \"B\"), strata = \"D\" ), biomarker = \"AGE\" ) #> $response #> [1] \"RSP\" #> #> $arm #> [1] \"AGE\" #> #> $covariates #> [1] \"A\" \"B\" #> #> $strata #> [1] \"D\" #> # For a single population, estimate separately the effects # of two biomarkers. df <- h_logistic_mult_cont_df( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\" ), data = adrs_f ) df #> biomarker biomarker_label n_tot n_rsp prop or lcl #> 1 BMRKR1 Continuous Level Biomarker 1 200 164 0.82 0.9755036 0.8804862 #> 2 AGE Age 200 164 0.82 0.9952416 0.9462617 #> ucl conf_level pval pval_label #> 1 1.080775 0.95 0.6352602 p-value (Wald) #> 2 1.046757 0.95 0.8530389 p-value (Wald) # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", strata = \"STRATA1\" ), data = adrs_f[NULL, ] ) #> biomarker biomarker_label n_tot n_tot_events median hr lcl ucl #> 1 BMRKR1 Continuous Level Biomarker 1 0 0 NA NA NA NA #> 2 AGE Age 0 0 NA NA NA NA #> conf_level pval pval_label #> 1 0.95 NA p-value (Wald) #> 2 0.95 NA p-value (Wald) # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_rsp_one_biomarker( df1, vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\") ) #> Total n Responders Response (%) Odds Ratio 95% CI p-value (Wald) #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> All patients 200 164 82.0% 0.98 (0.88, 1.08) 0.6353"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"Helper functions tabulate data frame statistics response rate odds ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"","code":"h_proportion_df(rsp, arm) h_proportion_subgroups_df( variables, data, groups_lists = list(), label_all = \"All Patients\" ) h_odds_ratio_df(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) h_odds_ratio_subgroups_df( variables, data, groups_lists = list(), conf_level = 0.95, method = NULL, label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"rsp (logical) vector indicating whether subject responder . arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame, NULL) required stratified analysis performed. conf_level (proportion) confidence level interval. method (string NULL) specifies test used calculate p-value difference two proportions. options, see test_proportion_diff(). Default NULL test performed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"h_proportion_df() returns data.frame columns arm, n, n_rsp, prop. h_proportion_subgroups_df() returns data.frame columns arm, n, n_rsp, prop, subgroup, var, var_label, row_type. h_odds_ratio_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, optionally pval pval_label. h_odds_ratio_subgroups_df() returns data.frame columns arm, n_tot, , lcl, ucl, conf_level, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"h_proportion_df(): Helper prepare data frame binary responses arm. h_proportion_subgroups_df(): Summarizes proportion binary responses arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_odds_ratio_df(): Helper prepare data frame estimates odds ratio treatment control arm. h_odds_ratio_subgroups_df(): Summarizes estimates odds ratio treatment control arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements rsp, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating binary response by subgroup — h_response_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") h_proportion_df( c(TRUE, FALSE, FALSE), arm = factor(c(\"A\", \"A\", \"B\"), levels = c(\"A\", \"B\")) ) #> arm n n_rsp prop #> 1 A 2 1 0.5 #> 2 B 1 0 0.0 h_proportion_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Define groupings for BMRKR2 levels. h_proportion_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 low BMRKR2 #> 8 A: Drug X 26 21 0.8076923 low BMRKR2 #> 9 B: Placebo 47 30 0.6382979 low/medium BMRKR2 #> 10 A: Drug X 52 44 0.8461538 low/medium BMRKR2 #> 11 B: Placebo 73 50 0.6849315 low/medium/high BMRKR2 #> 12 A: Drug X 69 59 0.8550725 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Unstratatified analysis. h_odds_ratio_df( c(TRUE, FALSE, FALSE, TRUE), arm = factor(c(\"A\", \"A\", \"B\", \"B\"), levels = c(\"A\", \"B\")) ) #> arm n_tot or lcl ucl conf_level #> 1 4 1 0.01984252 50.39681 0.95 # Include p-value. h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = \"chisq\") #> arm n_tot or lcl ucl conf_level pval #> 1 142 2.714 1.180449 6.239827 0.95 0.01643036 #> pval_label #> 1 p-value (Chi-Squared Test) # Stratatified analysis. h_odds_ratio_df( rsp = adrs_f$rsp, arm = adrs_f$ARM, strata_data = adrs_f[, c(\"STRATA1\", \"STRATA2\")], method = \"cmh\" ) #> arm n_tot or lcl ucl conf_level pval #> 1 142 2.665586 1.146149 6.199324 0.95 0.02019665 #> pval_label #> 1 p-value (Cochran-Mantel-Haenszel Test) # Unstratified analysis. h_odds_ratio_subgroups_df( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.1804488 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.2669576 51.452218 0.95 F SEX #> 3 64 0.920000 0.2966470 2.853223 0.95 M SEX #> 4 50 3.553846 1.0047370 12.570277 0.95 LOW BMRKR2 #> 5 49 2.705882 0.5911718 12.385232 0.95 MEDIUM BMRKR2 #> 6 43 2.250000 0.3970298 12.750933 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis # Stratified analysis. h_odds_ratio_subgroups_df( variables = list( rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = c(\"STRATA1\", \"STRATA2\") ), data = adrs_f ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.6655860 1.1461490 6.199324 0.95 All Patients ALL #> 2 78 7.7065093 1.5817529 37.547132 0.95 F SEX #> 3 64 0.9572284 0.2990954 3.063525 0.95 M SEX #> 4 50 3.0323726 0.8833232 10.409875 0.95 LOW BMRKR2 #> 5 49 2.1264996 0.4312008 10.486995 0.95 MEDIUM BMRKR2 #> 6 43 2.5134820 0.4351747 14.517370 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis # Define groupings of BMRKR2 levels. h_odds_ratio_subgroups_df( variables = list( rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.180449 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.266958 51.452218 0.95 F SEX #> 3 64 0.920000 0.296647 2.853223 0.95 M SEX #> 4 50 3.553846 1.004737 12.570277 0.95 low BMRKR2 #> 5 99 3.116667 1.193409 8.139385 0.95 low/medium BMRKR2 #> 6 142 2.714000 1.180449 6.239827 0.95 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data frame by subgroups — h_split_by_subgroups","title":"Split data frame by subgroups — h_split_by_subgroups","text":"Split data frame non-nested list subsets.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data frame by subgroups — h_split_by_subgroups","text":"","code":"h_split_by_subgroups(data, subgroups, groups_lists = list())"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data frame by subgroups — h_split_by_subgroups","text":"data (data.frame) dataset split. subgroups (character) names factor variables data used create subsets. Unused levels present data dropped. Note order vector determines order downstream table. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data frame by subgroups — h_split_by_subgroups","text":"list subset data (df) metadata subset (df_labels).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split data frame by subgroups — h_split_by_subgroups","text":"Main functionality prepare data use forest plot layouts.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_by_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data frame by subgroups — h_split_by_subgroups","text":"","code":"df <- data.frame( x = c(1:5), y = factor(c(\"A\", \"B\", \"A\", \"B\", \"A\"), levels = c(\"A\", \"B\", \"C\")), z = factor(c(\"C\", \"C\", \"D\", \"D\", \"D\"), levels = c(\"D\", \"C\")) ) formatters::var_labels(df) <- paste(\"label for\", names(df)) h_split_by_subgroups( data = df, subgroups = c(\"y\", \"z\") ) #> $y.A #> $y.A$df #> x y z #> 1 1 A C #> 2 3 A D #> 3 5 A D #> #> $y.A$df_labels #> subgroup var var_label #> 1 A y label for y #> #> #> $y.B #> $y.B$df #> x y z #> 1 2 B C #> 2 4 B D #> #> $y.B$df_labels #> subgroup var var_label #> 1 B y label for y #> #> #> $z.D #> $z.D$df #> x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #> #> $z.D$df_labels #> subgroup var var_label #> 1 D z label for z #> #> #> $z.C #> $z.C$df #> x y z #> 1 1 A C #> 2 2 B C #> #> $z.C$df_labels #> subgroup var var_label #> 1 C z label for z #> #> h_split_by_subgroups( data = df, subgroups = c(\"y\", \"z\"), groups_lists = list( y = list(\"AB\" = c(\"A\", \"B\"), \"C\" = \"C\") ) ) #> $y.AB #> $y.AB$df #> x y z #> 1 1 A C #> 2 2 B C #> 3 3 A D #> 4 4 B D #> 5 5 A D #> #> $y.AB$df_labels #> subgroup var var_label #> 1 AB y label for y #> #> #> $z.D #> $z.D$df #> x y z #> 1 3 A D #> 2 4 B D #> 3 5 A D #> #> $z.D$df_labels #> subgroup var var_label #> 1 D z label for z #> #> #> $z.C #> $z.C$df #> x y z #> 1 1 A C #> 2 2 B C #> #> $z.C$df_labels #> subgroup var var_label #> 1 C z label for z #> #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Split parameters — h_split_param","title":"Split parameters — h_split_param","text":"divides data vector param groups defined f based specified values. relevant rtables layers distribute parameters .stats ' .formats lists items corresponding specific analysis function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split parameters — h_split_param","text":"","code":"h_split_param(param, value, f)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split parameters — h_split_param","text":"param (vector) parameter split. value (vector) value used split. f (list) reference make split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split parameters — h_split_param","text":"named list element names f, containing elements specified .stats.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_split_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split parameters — h_split_param","text":"","code":"f <- list( surv = c(\"pt_at_risk\", \"event_free_rate\", \"rate_se\", \"rate_ci\"), surv_diff = c(\"rate_diff\", \"rate_diff_ci\", \"ztest_pval\") ) .stats <- c(\"pt_at_risk\", \"rate_diff\") h_split_param(.stats, .stats, f = f) #> $surv #> [1] \"pt_at_risk\" #> #> $surv_diff #> [1] \"rate_diff\" #> # $surv # [1] \"pt_at_risk\" # # $surv_diff # [1] \"rate_diff\" .formats <- c(\"pt_at_risk\" = \"xx\", \"event_free_rate\" = \"xxx\") h_split_param(.formats, names(.formats), f = f) #> $surv #> pt_at_risk event_free_rate #> \"xx\" \"xxx\" #> #> $surv_diff #> NULL #> # $surv # pt_at_risk event_free_rate # \"xx\" \"xxx\" # # $surv_diff # NULL"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"Helper function create new SMQ variable ADAE consists adverse events belonging selected Standardized/Customized queries. new dataset contain records adverse events belonging selected baskets. Remember na_str must match needed pre-processing done df_explicit_na() desired output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"h_stack_by_baskets( df, baskets = grep(\"^(SMQ|CQ).+NAM$\", names(df), value = TRUE), smq_varlabel = \"Standardized MedDRA Query\", keys = c(\"STUDYID\", \"USUBJID\", \"ASTDTM\", \"AEDECOD\", \"AESEQ\"), aag_summary = NULL, na_str = \"\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"df (data.frame) data set containing analysis variables. baskets (character) variable names selected Standardized/Customized queries. smq_varlabel (string) label new variable created. keys (character) names key variables returned along new variable created. aag_summary (data.frame) containing SMQ baskets levels interest final SMQ variable. useful levels interest observed df dataset. two columns dataset named basket basket_name. na_str (string) string used replace NA empty values output.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"data.frame variables keys taken df new variable SMQ containing records belonging baskets selected via baskets argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_stack_by_baskets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records. — h_stack_by_baskets","text":"","code":"adae <- tern_ex_adae[1:20, ] %>% df_explicit_na() h_stack_by_baskets(df = adae) #> # A tibble: 8 × 6 #> STUDYID USUBJID ASTDTM AEDECOD AESEQ SMQ #> #> 1 AB12345 AB12345-BRA-11-id-8 2021-12-05 02:02:07 dcd D.2.1.5.3 2 D.2.1.5… #> 2 AB12345 AB12345-BRA-12-id-120 2020-02-05 01:42:29 dcd D.2.1.5.3 2 D.2.1.5… #> 3 AB12345 AB12345-BRA-1-id-171 2022-11-29 12:18:31 dcd C.1.1.1.3 2 C.1.1.1… #> 4 AB12345 AB12345-BRA-1-id-23 2020-07-10 07:32:49 dcd B.2.2.3.1 3 C.1.1.1… #> 5 AB12345 AB12345-BRA-1-id-59 2021-10-10 23:54:46 dcd C.1.1.1.3 4 C.1.1.1… #> 6 AB12345 AB12345-BRA-1-id-9 2021-06-01 14:39:09 dcd C.1.1.1.3 1 C.1.1.1… #> 7 AB12345 AB12345-BRA-11-id-8 2021-12-21 02:02:07 dcd C.1.1.1.3 3 C.1.1.1… #> 8 AB12345 AB12345-BRA-12-id-120 2020-10-01 01:42:29 dcd C.1.1.1.3 3 C.1.1.1… aag <- data.frame( NAMVAR = c(\"CQ01NAM\", \"CQ02NAM\", \"SMQ01NAM\", \"SMQ02NAM\"), REFNAME = c( \"D.2.1.5.3/A.1.1.1.1 aesi\", \"X.9.9.9.9/Y.8.8.8.8 aesi\", \"C.1.1.1.3/B.2.2.3.1 aesi\", \"C.1.1.1.3/B.3.3.3.3 aesi\" ), SCOPE = c(\"\", \"\", \"BROAD\", \"BROAD\"), stringsAsFactors = FALSE ) basket_name <- character(nrow(aag)) cq_pos <- grep(\"^(CQ).+NAM$\", aag$NAMVAR) smq_pos <- grep(\"^(SMQ).+NAM$\", aag$NAMVAR) basket_name[cq_pos] <- aag$REFNAME[cq_pos] basket_name[smq_pos] <- paste0( aag$REFNAME[smq_pos], \"(\", aag$SCOPE[smq_pos], \")\" ) aag_summary <- data.frame( basket = aag$NAMVAR, basket_name = basket_name, stringsAsFactors = TRUE ) result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary) all(levels(aag_summary$basket_name) %in% levels(result$SMQ)) #> [1] TRUE h_stack_by_baskets( df = adae, aag_summary = NULL, keys = c(\"STUDYID\", \"USUBJID\", \"AEDECOD\", \"ARM\"), baskets = \"SMQ01NAM\" ) #> # A tibble: 6 × 5 #> STUDYID USUBJID AEDECOD ARM SMQ #> #> 1 AB12345 AB12345-BRA-1-id-171 dcd C.1.1.1.3 B: Placebo C.1.1.1.3/B.2.2.3.… #> 2 AB12345 AB12345-BRA-1-id-23 dcd B.2.2.3.1 A: Drug X C.1.1.1.3/B.2.2.3.… #> 3 AB12345 AB12345-BRA-1-id-59 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.… #> 4 AB12345 AB12345-BRA-1-id-9 dcd C.1.1.1.3 C: Combination C.1.1.1.3/B.2.2.3.… #> 5 AB12345 AB12345-BRA-11-id-8 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.… #> 6 AB12345 AB12345-BRA-12-id-120 dcd C.1.1.1.3 A: Drug X C.1.1.1.3/B.2.2.3.…"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"Helper functions used internally STEP calculations.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"","code":"h_step_window(x, control = control_step()) h_step_trt_effect(data, model, variables, x) h_step_survival_formula(variables, control = control_step()) h_step_survival_est( formula, data, variables, x, subset = rep(TRUE, nrow(data)), control = control_coxph() ) h_step_rsp_formula(variables, control = c(control_step(), control_logistic())) h_step_rsp_est( formula, data, variables, x, subset = rep(TRUE, nrow(data)), control = control_logistic() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"x (numeric) biomarker value(s) use (without NA). control (named list) output control_step(). data (data.frame) dataset containing variables summarize. model (coxph glm) regression model object. variables (named list string) list additional analysis variables. formula (formula) regression model formula. subset (logical) subset vector.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"h_step_window() returns list containing window-selection matrix sel interval information matrix interval. h_step_trt_effect() returns vector elements est se. h_step_survival_formula() returns model formula. h_step_survival_est() returns matrix number observations n, events, log hazard ratio estimates loghr, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x. h_step_rsp_formula() returns model formula. h_step_rsp_est() returns matrix number observations n, log odds ratio estimates logor, standard error se, Wald confidence interval bounds ci_lower ci_upper. One row included biomarker value x.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_step.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for subgroup treatment effect pattern (STEP) calculations — h_step","text":"h_step_window(): Creates windows STEP, based control settings provided. h_step_trt_effect(): Calculates estimated treatment effect estimate linear predictor scale corresponding standard error STEP model fitted data given variables specification, single biomarker value x. works coxph glm models, .e. calculating log hazard ratio log odds ratio estimates. h_step_survival_formula(): Builds model formula used survival STEP calculations. h_step_survival_est(): Estimates model formula built based variables data given subset control parameters Cox regression. h_step_rsp_formula(): Builds model formula used response STEP calculations. h_step_rsp_est(): Estimates model formula built based variables data given subset control parameters logistic regression.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"Helper functions documented separately confuse user reading user-facing functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"","code":"h_surv_to_coxreg_variables(variables, biomarker) h_coxreg_mult_cont_df(variables, data, control = control_coxreg()) h_tab_surv_one_biomarker( df, vars, time_unit, na_str = default_na_str(), .indent_mods = 0L, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"variables (named list string) list additional analysis variables. biomarker (string) name biomarker variable. data (data.frame) dataset containing variables summarize. control (list) list parameters returned helper function control_coxreg(). df (data.frame) results single biomarker, part returned extract_survival_biomarkers() (needs couple columns added high-level function relative returned h_coxreg_mult_cont_df(), see example). vars (character) names statistics reported among: n_tot_events: Total number events per group. n_tot: Total number observations per group. median: Median survival time. hr: Hazard ratio. ci: Confidence interval hazard ratio. pval: p-value effect. Note, one statistics n_tot n_tot_events, well hr ci required. time_unit (string) label unit median survival time. Default NULL skips displaying unit. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables() returns named list elements time, event, arm, covariates, strata. h_coxreg_mult_cont_df() returns data.frame containing estimates statistics selected biomarkers. h_tab_surv_one_biomarker() returns rtables table object given statistics arranged columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"h_surv_to_coxreg_variables(): Helps converting \"survival\" function variable list \"Cox regression\" variable list. reason currently inconsistency variable names accepted extract_survival_subgroups() fit_coxreg_multivar(). h_coxreg_mult_cont_df(): Prepares estimates number events, patients median survival times, well hazard ratio estimates, confidence intervals p-values, multiple biomarkers given single data set. variables corresponds names variables found data, passed named list requires elements tte, is_event, biomarkers (vector continuous biomarker variables) optionally subgroups strata. h_tab_surv_one_biomarker(): Prepares single sub-table given df_sub containing results single biomarker.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating biomarker effects on survival by subgroup — h_survival_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte, fill = FALSE) adtte_f <- adtte %>% filter(PARAMCD == \"OS\") %>% mutate( AVALU = as.character(AVALU), is_event = CNSR == 0 ) labels <- c(\"AVALU\" = adtte_labels[[\"AVALU\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels # This is how the variable list is converted internally. h_surv_to_coxreg_variables( variables = list( tte = \"AVAL\", is_event = \"EVNT\", covariates = c(\"A\", \"B\"), strata = \"D\" ), biomarker = \"AGE\" ) #> $time #> [1] \"AVAL\" #> #> $event #> [1] \"EVNT\" #> #> $arm #> [1] \"AGE\" #> #> $covariates #> [1] \"A\" \"B\" #> #> $strata #> [1] \"D\" #> # For a single population, estimate separately the effects # of two biomarkers. df <- h_coxreg_mult_cont_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f ) df #> biomarker biomarker_label n_tot n_tot_events median hr #> 1 BMRKR1 Continuous Level Biomarker 1 200 141 753.5176 1.000189 #> 2 AGE Age 200 141 753.5176 1.008267 #> lcl ucl conf_level pval pval_label #> 1 0.9511092 1.051802 0.95 0.9941244 p-value (Wald) #> 2 0.9845155 1.032591 0.95 0.4984743 p-value (Wald) # If the data set is empty, still the corresponding rows with missings are returned. h_coxreg_mult_cont_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"REGION1\", strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f[NULL, ] ) #> biomarker biomarker_label n_tot n_tot_events median hr lcl ucl #> 1 BMRKR1 Continuous Level Biomarker 1 0 0 NA NA NA NA #> 2 AGE Age 0 0 NA NA NA NA #> conf_level pval pval_label #> 1 0.95 NA p-value (Wald) #> 2 0.95 NA p-value (Wald) # Starting from above `df`, zoom in on one biomarker and add required columns. df1 <- df[1, ] df1$subgroup <- \"All patients\" df1$row_type <- \"content\" df1$var <- \"ALL\" df1$var_label <- \"All patients\" h_tab_surv_one_biomarker( df1, vars = c(\"n_tot\", \"n_tot_events\", \"median\", \"hr\", \"ci\", \"pval\"), time_unit = \"days\" ) #> Total n Total Events Median (days) Hazard Ratio 95% Wald CI p-value (Wald) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— #> All patients 200 141 753.5 1.00 (0.95, 1.05) 0.9941"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"Helper functions tabulate data frame statistics median survival time hazard ratio population subgroups.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"","code":"h_survtime_df(tte, is_event, arm) h_survtime_subgroups_df( variables, data, groups_lists = list(), label_all = \"All Patients\" ) h_coxph_df(tte, is_event, arm, strata_data = NULL, control = control_coxph()) h_coxph_subgroups_df( variables, data, groups_lists = list(), control = control_coxph(), label_all = \"All Patients\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"tte (numeric) vector time--event duration values. is_event (flag)TRUE event, FALSE time event censored. arm (factor) treatment group variable. variables (named list string) list additional analysis variables. data (data.frame) dataset containing variables summarize. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. strata_data (factor, data.frame, NULL) required stratified analysis performed. control (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing null hypothesis hazard ratio = 1. Default method \"log-rank\" comes survival::survdiff(), can also set \"wald\" \"likelihood\" (survival::coxph()). ties (string) specifying method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph(). conf_level (proportion) confidence level interval HR.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"h_survtime_df() returns data.frame columns arm, n, n_events, median. h_survtime_subgroups_df() returns data.frame columns arm, n, n_events, median, subgroup, var, var_label, row_type. h_coxph_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval pval_label. h_coxph_subgroups_df() returns data.frame columns arm, n_tot, n_tot_events, hr, lcl, ucl, conf_level, pval, pval_label, subgroup, var, var_label, row_type.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"Main functionality prepare data use layout-creating function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"h_survtime_df(): Helper prepare data frame median survival times arm. h_survtime_subgroups_df(): Summarizes median survival times arm across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups. groups_lists optionally specifies groupings subgroups variables. h_coxph_df(): Helper prepare data frame estimates treatment hazard ratio. h_coxph_subgroups_df(): Summarizes estimates treatment hazard ratio across subgroups data frame. variables corresponds names variables found data, passed named list requires elements tte, is_event, arm optionally subgroups strata. groups_lists optionally specifies groupings subgroups variables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_survival_duration_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for tabulating survival duration by subgroup — h_survival_duration_subgroups","text":"","code":"library(dplyr) library(forcats) adtte <- tern_ex_adtte # Save variable labels before data processing steps. adtte_labels <- formatters::var_labels(adtte) adtte_f <- adtte %>% filter( PARAMCD == \"OS\", ARM %in% c(\"B: Placebo\", \"A: Drug X\"), SEX %in% c(\"M\", \"F\") ) %>% mutate( # Reorder levels of ARM to display reference arm before treatment arm. ARM = droplevels(fct_relevel(ARM, \"B: Placebo\")), SEX = droplevels(SEX), is_event = CNSR == 0 ) labels <- c(\"ARM\" = adtte_labels[[\"ARM\"]], \"SEX\" = adtte_labels[[\"SEX\"]], \"is_event\" = \"Event Flag\") formatters::var_labels(adtte_f)[names(labels)] <- labels # Extract median survival time for one group. h_survtime_df( tte = adtte_f$AVAL, is_event = adtte_f$is_event, arm = adtte_f$ARM ) #> arm n n_events median #> 1 B: Placebo 73 57 727.8043 #> 2 A: Drug X 69 44 974.6402 # Extract median survival time for multiple groups. h_survtime_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) #> arm n n_events median subgroup var #> 1 B: Placebo 73 57 727.8043 All Patients ALL #> 2 A: Drug X 69 44 974.6402 All Patients ALL #> 3 B: Placebo 40 31 599.1772 F SEX #> 4 A: Drug X 38 24 1016.2982 F SEX #> 5 B: Placebo 33 26 888.4916 M SEX #> 6 A: Drug X 31 20 974.6402 M SEX #> 7 B: Placebo 24 21 735.4722 LOW BMRKR2 #> 8 A: Drug X 26 15 974.6402 LOW BMRKR2 #> 9 B: Placebo 23 14 731.8352 MEDIUM BMRKR2 #> 10 A: Drug X 26 17 964.2197 MEDIUM BMRKR2 #> 11 B: Placebo 26 22 654.8245 HIGH BMRKR2 #> 12 A: Drug X 17 12 1016.2982 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Define groupings for BMRKR2 levels. h_survtime_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n n_events median subgroup var #> 1 B: Placebo 73 57 727.8043 All Patients ALL #> 2 A: Drug X 69 44 974.6402 All Patients ALL #> 3 B: Placebo 40 31 599.1772 F SEX #> 4 A: Drug X 38 24 1016.2982 F SEX #> 5 B: Placebo 33 26 888.4916 M SEX #> 6 A: Drug X 31 20 974.6402 M SEX #> 7 B: Placebo 24 21 735.4722 low BMRKR2 #> 8 A: Drug X 26 15 974.6402 low BMRKR2 #> 9 B: Placebo 47 35 735.4722 low/medium BMRKR2 #> 10 A: Drug X 52 32 964.2197 low/medium BMRKR2 #> 11 B: Placebo 73 57 727.8043 low/medium/high BMRKR2 #> 12 A: Drug X 69 44 974.6402 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis # Extract hazard ratio for one group. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.057337 0.95 0.09049511 #> pval_label #> 1 p-value (log-rank) # Extract hazard ratio for one group with stratification factor. h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.6646586 0.4399495 1.00414 0.95 0.05089188 #> pval_label #> 1 p-value (log-rank) # Extract hazard ratio for multiple groups. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> 2 78 55 0.5595391 0.3246658 0.9643271 0.95 0.03411759 #> 3 64 46 0.9102874 0.5032732 1.6464678 0.95 0.75582028 #> 4 50 36 0.7617717 0.3854349 1.5055617 0.95 0.43236030 #> 5 49 31 0.7651261 0.3641277 1.6077269 0.95 0.47860004 #> 6 43 34 0.6662356 0.3257413 1.3626456 0.95 0.26285846 #> pval_label subgroup var var_label row_type #> 1 p-value (log-rank) All Patients ALL All Patients content #> 2 p-value (log-rank) F SEX Sex analysis #> 3 p-value (log-rank) M SEX Sex analysis #> 4 p-value (log-rank) LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank) MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank) HIGH BMRKR2 Continuous Level Biomarker 2 analysis # Define groupings of BMRKR2 levels. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\") ), data = adtte_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> 2 78 55 0.5595391 0.3246658 0.9643271 0.95 0.03411759 #> 3 64 46 0.9102874 0.5032732 1.6464678 0.95 0.75582028 #> 4 50 36 0.7617717 0.3854349 1.5055617 0.95 0.43236030 #> 5 99 67 0.7472958 0.4600419 1.2139136 0.95 0.23764314 #> 6 142 101 0.7108557 0.4779138 1.0573368 0.95 0.09049511 #> pval_label subgroup var var_label #> 1 p-value (log-rank) All Patients ALL All Patients #> 2 p-value (log-rank) F SEX Sex #> 3 p-value (log-rank) M SEX Sex #> 4 p-value (log-rank) low BMRKR2 Continuous Level Biomarker 2 #> 5 p-value (log-rank) low/medium BMRKR2 Continuous Level Biomarker 2 #> 6 p-value (log-rank) low/medium/high BMRKR2 Continuous Level Biomarker 2 #> row_type #> 1 content #> 2 analysis #> 3 analysis #> 4 analysis #> 5 analysis #> 6 analysis # Extract hazard ratio for multiple groups with stratification factors. h_coxph_subgroups_df( variables = list( tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = c(\"STRATA1\", \"STRATA2\") ), data = adtte_f ) #> arm n_tot n_tot_events hr lcl ucl conf_level pval #> 1 142 101 0.6126133 0.3913507 0.9589739 0.95 0.03086774 #> 2 78 55 0.3934024 0.2027682 0.7632630 0.95 0.00469167 #> 3 64 46 0.9501768 0.4730073 1.9087145 0.95 0.88580522 #> 4 50 36 0.7378635 0.3140465 1.7336363 0.95 0.48408079 #> 5 49 31 0.9408062 0.4172095 2.1215148 0.95 0.88305965 #> 6 43 34 0.5125617 0.2125140 1.2362459 0.95 0.13124382 #> pval_label subgroup var var_label row_type #> 1 p-value (log-rank) All Patients ALL All Patients content #> 2 p-value (log-rank) F SEX Sex analysis #> 3 p-value (log-rank) M SEX Sex analysis #> 4 p-value (log-rank) LOW BMRKR2 Continuous Level Biomarker 2 analysis #> 5 p-value (log-rank) MEDIUM BMRKR2 Continuous Level Biomarker 2 analysis #> 6 p-value (log-rank) HIGH BMRKR2 Continuous Level Biomarker 2 analysis"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"Please see h_tab_surv_one_biomarker() h_tab_rsp_one_biomarker(), use function examples. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"","code":"h_tab_one_biomarker( df, afuns, colvars, na_str = default_na_str(), .indent_mods = 0L, ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"df (data.frame) results single biomarker. afuns (named list function) analysis functions. colvars (named list) named list elements vars (variables tabulate) labels (labels). na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. ... additional arguments lower level functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tab_one_biomarker.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for tabulation of a single biomarker result — h_tab_one_biomarker","text":"rtables table object statistics columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"Create data.frame pairwise stratified unstratified Cox-PH analysis results.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"","code":"h_tbl_coxph_pairwise( df, variables, ref_group_coxph = NULL, control_coxph_pw = control_coxph(), annot_coxph_ref_lbls = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"df (data.frame) data set containing analysis variables. variables (named list) variable names. Details : tte (numeric) variable indicating time--event duration values. is_event (logical) event variable. TRUE event, FALSE time event censored. arm (factor) treatment group variable. strata (character NULL) variable names indicating stratification factors. ref_group_coxph (string NULL) level arm variable use reference group calculations annot_coxph table. NULL (default), uses first level arm variable. control_coxph_pw (list) parameters comparison details, specified using helper function control_coxph(). possible parameter options : pval_method (string) p-value method testing hazard ratio = 1. Default method \"log-rank\", can also set \"wald\" \"likelihood\". ties (string) method tie handling. Default \"efron\", can also set \"breslow\" \"exact\". See survival::coxph() conf_level (proportion) confidence level interval HR. annot_coxph_ref_lbls (flag) whether reference group explicitly printed labels annot_coxph table. FALSE (default), comparison groups printed annot_coxph table labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"data.frame containing statistics HR, XX% CI (XX taken control_coxph_pw), p-value (log-rank).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_coxph_pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for generating a pairwise Cox-PH table — h_tbl_coxph_pairwise","text":"","code":"library(dplyr) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% mutate(is_event = CNSR == 0) h_tbl_coxph_pairwise( df = adtte, variables = list(tte = \"AVAL\", is_event = \"is_event\", arm = \"ARM\"), control_coxph_pw = control_coxph(conf_level = 0.9) ) #> HR 90% CI p-value (log-rank) #> B: Placebo 1.41 (1.01, 1.96) 0.0905 #> C: Combination 1.81 (1.24, 2.64) 0.0086"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for survival estimations — h_tbl_median_surv","title":"Helper function for survival estimations — h_tbl_median_surv","text":"Transform survival fit table groups rows characterized N, median confidence interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for survival estimations — h_tbl_median_surv","text":"","code":"h_tbl_median_surv(fit_km, armval = \"All\")"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for survival estimations — h_tbl_median_surv","text":"fit_km (survfit) result survival::survfit(). armval (string) used strata name treatment arm variable one level. Default \"\".","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for survival estimations — h_tbl_median_surv","text":"summary table statistics N, Median, XX% CI (XX taken fit_km).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_tbl_median_surv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for survival estimations — h_tbl_median_surv","text":"","code":"library(dplyr) library(survival) adtte <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") fit <- survfit( formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = adtte ) h_tbl_median_surv(fit_km = fit) #> N Median 95% CI #> ARM A 69 974.6 (687, 1625) #> ARM B 73 727.8 (555.8, 1156) #> ARM C 58 632.3 (393, 1001)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"Helper function count number patients fraction patients according highest post-baseline lab grade variable .var, baseline lab grade variable baseline_var, direction interest specified direction_var.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"h_worsen_counter(df, id, .var, baseline_var, direction_var)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"df (data.frame) data set containing analysis variables. id (string) subject variable name. .var (string) single variable name passed rtables requested statistics function. baseline_var (string) name baseline lab grade variable. direction_var (string) name direction variable specifying direction shift table interest. lab records flagged L, H B included shift table. L: low direction H: high direction B: low high directions","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"counts fraction patients whose worst post-baseline lab grades worse baseline grades, post-baseline worst grades \"1\", \"2\", \"3\", \"4\" \"\".","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/h_worsen_counter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to analyze patients for s_count_abnormal_lab_worsen_by_baseline() — h_worsen_counter","text":"","code":"library(dplyr) # The direction variable, GRADDR, is based on metadata adlb <- tern_ex_adlb %>% mutate( GRADDR = case_when( PARAMCD == \"ALT\" ~ \"B\", PARAMCD == \"CRP\" ~ \"L\", PARAMCD == \"IGA\" ~ \"H\" ) ) %>% filter(SAFFL == \"Y\" & ONTRTFL == \"Y\" & GRADDR != \"\") df <- h_adlb_worsen( adlb, worst_flag_low = c(\"WGRLOFL\" = \"Y\"), worst_flag_high = c(\"WGRHIFL\" = \"Y\"), direction_var = \"GRADDR\" ) # `h_worsen_counter` h_worsen_counter( df %>% filter(PARAMCD == \"CRP\" & GRADDR == \"Low\"), id = \"USUBJID\", .var = \"ATOXGR\", baseline_var = \"BTOXGR\", direction_var = \"GRADDR\" ) #> $fraction #> $fraction$`1` #> num denom #> 25 180 #> #> $fraction$`2` #> num denom #> 15 186 #> #> $fraction$`3` #> num denom #> 18 191 #> #> $fraction$`4` #> num denom #> 17 196 #> #> $fraction$Any #> num denom #> 75 196 #> #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to calculate x-tick positions — h_xticks","title":"Helper function to calculate x-tick positions — h_xticks","text":"Calculate positions ticks x-axis. However, xticks already exists kept . based function ggplot2 relies , required graphic patient--risk annotation table.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to calculate x-tick positions — h_xticks","text":"","code":"h_xticks(data, xticks = NULL, max_time = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to calculate x-tick positions — h_xticks","text":"data (data.frame) survival data pre-processed h_data_plot. xticks (numeric NULL) numeric vector tick positions single number spacing ticks x-axis. NULL (default), labeling::extended() used determine optimal tick positions x-axis. max_time (numeric(1)) maximum value show x-axis. data values less threshold value plotted (defaults NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to calculate x-tick positions — h_xticks","text":"vector positions use x-axis ticks ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/h_xticks.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to calculate x-tick positions — h_xticks","text":"","code":"library(dplyr) library(survival) data <- tern_ex_adtte %>% filter(PARAMCD == \"OS\") %>% survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>% h_data_plot() h_xticks(data) #> [1] 0 1000 2000 3000 4000 5000 h_xticks(data, xticks = seq(0, 3000, 500)) #> [1] 0 500 1000 1500 2000 2500 3000 h_xticks(data, xticks = 500) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500 h_xticks(data, xticks = 500, max_time = 6000) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 h_xticks(data, xticks = c(0, 500), max_time = 300) #> [1] 0 500 h_xticks(data, xticks = 500, max_time = 300) #> [1] 0 500 1000 1500 2000 2500 3000 3500 4000 4500"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/imputation_rule.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply 1/3 or 1/2 imputation rule to data — imputation_rule","text":"","code":"imputation_rule( df, x_stats, stat, imp_rule, post = FALSE, avalcat_var = \"AVALCAT1\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/imputation_rule.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply 1/3 or 1/2 imputation rule to data — imputation_rule","text":"df (data.frame) data set containing analysis variables. x_stats (named list) named list statistics, typically results s_summary(). stat (string) statistic return value/NA level according imputation rule applied. imp_rule (string) imputation rule setting. Set \"1/3\" implement 1/3 imputation rule \"1/2\" implement 1/2 imputation rule. post (flag) whether data corresponds post-dose time-point (defaults FALSE). parameter used imp_rule set \"1/3\". avalcat_var (string) name variable indicates whether row df corresponds analysis value category \"BLQ\", \"LTR\", \" $val #> max #> 0.9919061 #> #> $na_str #> [1] \"ND\" #> imputation_rule(df, x_stats, \"geom_mean\", \"1/3\") #> $val #> [1] NA #> #> $na_str #> [1] \"NE\" #> imputation_rule(df, x_stats, \"mean\", \"1/2\") #> $val #> [1] NA #> #> $na_str #> [1] \"ND\" #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence rate estimation — incidence_rate","title":"Incidence rate estimation — incidence_rate","text":"analyze function estimate_incidence_rate() creates layout element estimate event rate adjusted person-years risk, otherwise known incidence rate. primary analysis variable specified via vars person-years risk. addition variable, n_events variable number events observed (value 1 means event observed 0 means event observed) must also specified.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence rate estimation — incidence_rate","text":"","code":"estimate_incidence_rate( lyt, vars, n_events, id_var = \"USUBJID\", control = control_incidence_rate(), na_str = default_na_str(), nested = TRUE, summarize = FALSE, label_fmt = \"%s - %.labels\", ..., show_labels = \"hidden\", table_names = vars, .stats = c(\"person_years\", \"n_events\", \"rate\", \"rate_ci\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_incidence_rate( df, .var, n_events, is_event = lifecycle::deprecated(), id_var = \"USUBJID\", control = control_incidence_rate() ) a_incidence_rate( df, labelstr = \"\", .var, .df_row, n_events, id_var = \"USUBJID\", control = control_incidence_rate(), .stats = NULL, .formats = c(person_years = \"xx.x\", n_events = \"xx\", rate = \"xx.xx\", rate_ci = \"(xx.xx, xx.xx)\", n_unique = \"xx\", n_rate = \"xx (xx.x)\"), .labels = NULL, .indent_mods = NULL, na_str = default_na_str(), label_fmt = \"%s - %.labels\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence rate estimation — incidence_rate","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . n_events (string) name integer variable indicating whether event observed (1) (0). id_var (string) name variable used patient identifier \"n_unique\" included .stats. Defaults \"USUBJID\". control (list) parameters estimation details, specified using helper function control_incidence_rate(). Possible parameter options : conf_level (proportion) confidence level estimated incidence rate. conf_type (string)normal (default), normal_log, exact, byar confidence interval type. input_time_unit (string)day, week, month, year (default) indicating time unit data input. num_pt_year (numeric) time unit desired output (person-years). na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. summarize (flag) whether function act analyze function (summarize = FALSE), summarize function (summarize = TRUE). Defaults FALSE. label_fmt (string) labels formatted row split occurs summarize = TRUE. string use \"%s\" represent row split levels, \"%.labels\" represent labels supplied .labels argument. Defaults \"%s - %.labels\". ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_incidence_rate\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. is_event (flag)TRUE event, FALSE time event censored. labelstr (string) label level parent split currently summarized (must present second argument Content Row Functions). See rtables::summarize_row_groups() information. .df_row (data.frame) data frame across columns given row split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence rate estimation — incidence_rate","text":"estimate_incidence_rate() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_incidence_rate() table layout. s_incidence_rate() returns following statistics: person_years: Total person-years risk. n_events: Total number events observed. rate: Estimated incidence rate. rate_ci: Confidence interval incidence rate. n_unique: Total number patients least one event observed. n_rate: Total number events observed & estimated incidence rate. a_incidence_rate() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Incidence rate estimation — incidence_rate","text":"estimate_incidence_rate(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_incidence_rate(): Statistics function estimates incidence rate associated confidence interval. a_incidence_rate(): Formatted analysis function used afun estimate_incidence_rate().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/incidence_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence rate estimation — incidence_rate","text":"","code":"df <- data.frame( USUBJID = as.character(seq(6)), CNSR = c(0, 1, 1, 0, 0, 0), AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4), ARM = factor(c(\"A\", \"A\", \"A\", \"B\", \"B\", \"B\")), STRATA1 = factor(c(\"X\", \"Y\", \"Y\", \"X\", \"X\", \"Y\")) ) df$n_events <- 1 - df$CNSR basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% estimate_incidence_rate( vars = \"AVAL\", n_events = \"n_events\", control = control_incidence_rate( input_time_unit = \"month\", num_pt_year = 100 ) ) %>% build_table(df) #> A B #> (N=3) (N=3) #> ————————————————————————————————————————————————————————————————————— #> Total patient-years at risk 3.8 5.2 #> Number of adverse events observed 1 3 #> AE rate per 100 patient-years 26.20 57.23 #> 95% CI (-25.15, 77.55) (-7.53, 122.00) # summarize = TRUE basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\", child_labels = \"visible\") %>% estimate_incidence_rate( vars = \"AVAL\", n_events = \"n_events\", .stats = c(\"n_unique\", \"n_rate\"), summarize = TRUE, label_fmt = \"%.labels\" ) %>% build_table(df) #> A B #> (N=3) (N=3) #> ——————————————————————————————————————————————————————————————————————————————————————— #> X #> Total number of patients with at least one adverse event 1 2 #> Number of adverse events observed (AE rate per 100 patient-years) 1 (9.9) 2 (5.1) #> Y #> Total number of patients with at least one adverse event 0 1 #> Number of adverse events observed (AE rate per 100 patient-years) 0 (0.0) 1 (4.3) a_incidence_rate( df, .var = \"AVAL\", .df_row = df, n_events = \"n_events\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod #> 1 person_years 108.7 0 #> 2 n_events 4 0 #> 3 rate 3.68 0 #> 4 rate_ci (0.07, 7.29) 0 #> 5 n_unique 4 0 #> 6 n_rate 4 (3.7) 0 #> row_label #> 1 Total patient-years at risk #> 2 Number of adverse events observed #> 3 AE rate per 100 patient-years #> 4 95% CI #> 5 Total number of patients with at least one adverse event #> 6 Number of adverse events observed (AE rate per 100 patient-years)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels or names of list elements — labels_or_names","title":"Labels or names of list elements — labels_or_names","text":"Internal helper function working nested statistic function results typically labels names can use.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels or names of list elements — labels_or_names","text":"","code":"labels_or_names(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Labels or names of list elements — labels_or_names","text":"x (list) list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_or_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Labels or names of list elements — labels_or_names","text":"character vector labels names list elements.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":null,"dir":"Reference","previous_headings":"","what":"Update labels according to control specifications — labels_use_control","title":"Update labels according to control specifications — labels_use_control","text":"Given list statistic labels list control parameters, updates labels relevant control specification. example, control element conf_level set 0.9, default label statistic mean_ci updated \"Mean 90% CI\". labels supplied via labels_custom updated regardless control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update labels according to control specifications — labels_use_control","text":"","code":"labels_use_control(labels_default, control, labels_custom = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update labels according to control specifications — labels_use_control","text":"labels_default (named character) named vector statistic labels modify according control specifications. Labels explicitly defined labels_custom affected. control (named list) list control parameters apply adjust default labels. labels_custom (named character) named vector labels customized user affected control.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update labels according to control specifications — labels_use_control","text":"named character vector labels control specifications applied relevant labels.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/labels_use_control.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update labels according to control specifications — labels_use_control","text":"","code":"control <- list(conf_level = 0.80, quantiles = c(0.1, 0.83), test_mean = 0.57) get_labels_from_stats(c(\"mean_ci\", \"quantiles\", \"mean_pval\")) %>% labels_use_control(control = control) #> mean_ci quantiles #> \"Mean 80% CI\" \"10% and 83%-ile\" #> mean_pval #> \"Mean p-value (H0: mean = 0.57)\""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic regression multivariate column layout function — logistic_regression_cols","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"Layout-creating function creates multivariate column layout summarizing logistic regression results. function wrapper rtables::split_cols_by_multivar().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"","code":"logistic_regression_cols(lyt, conf_level = 0.95)"},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"lyt (PreDataTableLayouts) layout analyses added . conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_regression_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic regression multivariate column layout function — logistic_regression_cols","text":"layout object suitable passing layouting functions. Adding function rtable layout split table columns corresponding statistics df, estimate, std_error, odds_ratio, ci, pvalue.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":null,"dir":"Reference","previous_headings":"","what":"Logistic regression summary table — logistic_summary_by_flag","title":"Logistic regression summary table — logistic_summary_by_flag","text":"Constructor content functions used summarize_logistic() summarize logistic regression results. function wrapper rtables::summarize_row_groups().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logistic regression summary table — logistic_summary_by_flag","text":"","code":"logistic_summary_by_flag( flag_var, na_str = default_na_str(), .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logistic regression summary table — logistic_summary_by_flag","text":"flag_var (string) variable name identifying row used content function. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/logistic_summary_by_flag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logistic regression summary table — logistic_summary_by_flag","text":"content function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Make names without dots — make_names","title":"Make names without dots — make_names","text":"Make names without dots","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make names without dots — make_names","text":"","code":"make_names(nams)"},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make names without dots — make_names","text":"nams (character) vector original names.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/make_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make names without dots — make_names","text":"character vector proper names, use dots contrast make.names().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":null,"dir":"Reference","previous_headings":"","what":"Conversion of months to days — month2day","title":"Conversion of months to days — month2day","text":"Conversion months days. approximative calculation considers month average 30.4375 days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conversion of months to days — month2day","text":"","code":"month2day(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conversion of months to days — month2day","text":"x (numeric(1)) time months.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conversion of months to days — month2day","text":"numeric vector time days.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/month2day.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conversion of months to days — month2day","text":"","code":"x <- c(13.25, 8.15, 1, 2.834) month2day(x) #> [1] 403.29688 248.06563 30.43750 86.25988"},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":null,"dir":"Reference","previous_headings":"","what":"Muffled car::Anova — muffled_car_anova","title":"Muffled car::Anova — muffled_car_anova","text":"Applied survival models, car::Anova() signal strata terms dropped model formula present, function deliberately muffles message.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Muffled car::Anova — muffled_car_anova","text":"","code":"muffled_car_anova(mod, test_statistic)"},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Muffled car::Anova — muffled_car_anova","text":"mod (coxph) Cox regression model fitted survival::coxph(). test_statistic (string) method used estimation p.values; wald (default) likelihood.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/muffled_car_anova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Muffled car::Anova — muffled_car_anova","text":"output car::Anova(), convergence message muffled.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of available (non-missing entries) in a vector — n_available","title":"Number of available (non-missing entries) in a vector — n_available","text":"Small utility function better readability.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of available (non-missing entries) in a vector — n_available","text":"","code":"n_available(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of available (non-missing entries) in a vector — n_available","text":"x (vector) vector count non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/n_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of available (non-missing entries) in a vector — n_available","text":"Number non-missing values.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Odds ratio estimation — odds_ratio","title":"Odds ratio estimation — odds_ratio","text":"analyze function estimate_odds_ratio() creates layout element compare bivariate responses two groups estimating odds ratio confidence interval. primary analysis variable specified vars group variable. Additional variables can included analysis via variables argument, accepts arm, arm variable, strata, stratification variable. two arm levels present, can combined two groups using groups_list argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Odds ratio estimation — odds_ratio","text":"","code":"estimate_odds_ratio( lyt, vars, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"hidden\", table_names = vars, .stats = \"or_ci\", .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_odds_ratio( df, .var, .ref_group, .in_ref_col, .df_row, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL ) a_odds_ratio( df, .var, .ref_group, .in_ref_col, .df_row, variables = list(arm = NULL, strata = NULL), conf_level = 0.95, groups_list = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Odds ratio estimation — odds_ratio","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... arguments passed s_odds_ratio(). show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_odds_ratio\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise. .df_row (data.frame) data frame across columns given row split.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Odds ratio estimation — odds_ratio","text":"estimate_odds_ratio() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_odds_ratio() table layout. s_odds_ratio() returns named list statistics or_ci (containing est, lcl, ucl) n_tot. a_odds_ratio() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Odds ratio estimation — odds_ratio","text":"estimate_odds_ratio(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_odds_ratio(): Statistics function estimates odds ratio treatment control. variables list arm strata variable names must passed stratified analysis required. a_odds_ratio(): Formatted analysis function used afun estimate_odds_ratio().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Odds ratio estimation — odds_ratio","text":"function uses logistic regression unstratified analyses, conditional logistic regression stratified analyses. Wald confidence interval calculated specified confidence level. stratified analyses, currently implementation conditional likelihood confidence intervals, therefore likelihood confidence interval available option. vars contains responders non-responders odds ratio estimation possible returned values NA.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/odds_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Odds ratio estimation — odds_ratio","text":"","code":"set.seed(12) dta <- data.frame( rsp = sample(c(TRUE, FALSE), 100, TRUE), grp = factor(rep(c(\"A\", \"B\"), each = 50), levels = c(\"A\", \"B\")), strata = factor(sample(c(\"C\", \"D\"), 100, TRUE)) ) l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% estimate_odds_ratio(vars = \"rsp\") build_table(l, df = dta) #> A B #> ———————————————————————————————————————————— #> Odds Ratio (95% CI) 0.85 (0.38 - 1.88) # Unstratified analysis. s_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta ) #> $or_ci #> est lcl ucl #> 0.8484848 0.3831831 1.8788053 #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #> #> $n_tot #> n_tot #> 100 #> attr(,\"label\") #> [1] \"Total n\" #> # Stratified analysis. s_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta, variables = list(arm = \"grp\", strata = \"strata\") ) #> $or_ci #> est lcl ucl #> 0.7689750 0.3424155 1.7269154 #> attr(,\"label\") #> [1] \"Odds Ratio (95% CI)\" #> #> $n_tot #> n_tot #> 100 #> attr(,\"label\") #> [1] \"Total n\" #> a_odds_ratio( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, .df_row = dta ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 or_ci 0.85 (0.38 - 1.88) 1 Odds Ratio (95% CI) #> 2 n_tot 100 0 Total n"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion difference estimation — prop_diff","title":"Proportion difference estimation — prop_diff","text":"analysis function estimate_proportion_diff() creates layout element estimate difference proportion responders within studied population. primary analysis variable, vars, logical variable indicating whether response occurred record. See method parameter options methods use constructing confidence interval proportion difference. stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion difference estimation — prop_diff","text":"","code":"estimate_proportion_diff( lyt, vars, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\", na_str = default_na_str(), nested = TRUE, ..., var_labels = vars, show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\" ) a_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), conf_level = 0.95, method = c(\"waldcc\", \"wald\", \"cmh\", \"ha\", \"newcombe\", \"newcombecc\", \"strat_newcombe\", \"strat_newcombecc\"), weights_method = \"cmh\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion difference estimation — prop_diff","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. conf_level (proportion) confidence level interval. method (string) method used confidence interval estimation. weights_method (string) weights method. Can either \"cmh\" \"heuristic\" directs way weights estimated. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"estimate_proportion_diff\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion difference estimation — prop_diff","text":"estimate_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_proportion_diff() table layout. s_proportion_diff() returns named list elements diff diff_ci. a_proportion_diff() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Proportion difference estimation — prop_diff","text":"estimate_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_proportion_diff(): Statistics function estimating difference terms responder proportion. a_proportion_diff(): Formatted analysis function used afun estimate_proportion_diff().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Proportion difference estimation — prop_diff","text":"performing unstratified analysis, methods \"cmh\", \"strat_newcombe\", \"strat_newcombecc\" permitted.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion difference estimation — prop_diff","text":"","code":"## \"Mid\" case: 4/4 respond in group A, 1/2 respond in group B. nex <- 100 # Number of example rows dta <- data.frame( \"rsp\" = sample(c(TRUE, FALSE), nex, TRUE), \"grp\" = sample(c(\"A\", \"B\"), nex, TRUE), \"f1\" = sample(c(\"a1\", \"a2\"), nex, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), nex, TRUE), stringsAsFactors = TRUE ) l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% estimate_proportion_diff( vars = \"rsp\", conf_level = 0.90, method = \"ha\" ) build_table(l, df = dta) #> A B #> —————————————————————————————————————————————————— #> Difference in Response rate (%) 12.0 #> 90% CI (Anderson-Hauck) (-5.4, 29.4) s_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, conf_level = 0.90, method = \"ha\" ) #> $diff #> [1] 12 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #> #> $diff_ci #> [1] -5.374519 29.374519 #> attr(,\"label\") #> [1] \"90% CI (Anderson-Hauck)\" #> # CMH example with strata s_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, variables = list(strata = c(\"f1\", \"f2\")), conf_level = 0.90, method = \"cmh\" ) #> $diff #> [1] 12.05847 #> attr(,\"label\") #> [1] \"Difference in Response rate (%)\" #> #> $diff_ci #> [1] -2.67057 26.78750 #> attr(,\"label\") #> [1] \"90% CI (CMH, without correction)\" #> a_proportion_diff( df = subset(dta, grp == \"A\"), .var = \"rsp\", .ref_group = subset(dta, grp == \"B\"), .in_ref_col = FALSE, conf_level = 0.90, method = \"ha\" ) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 diff 12.0 0 Difference in Response rate (%) #> 2 diff_ci (-5.4, 29.4) 1 90% CI (Anderson-Hauck)"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Difference test for two proportions — prop_diff_test","title":"Difference test for two proportions — prop_diff_test","text":"analyze function test_proportion_diff() creates layout element test difference two proportions. primary analysis variable, vars, indicates whether response occurred record. See method parameter options methods use calculate p-value. Additionally, stratification variable can supplied via strata element variables argument.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Difference test for two proportions — prop_diff_test","text":"","code":"test_proportion_diff( lyt, vars, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\"), na_str = default_na_str(), nested = TRUE, ..., var_labels = vars, show_labels = \"hidden\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_test_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") ) a_test_proportion_diff( df, .var, .ref_group, .in_ref_col, variables = list(strata = NULL), method = c(\"chisq\", \"schouten\", \"fisher\", \"cmh\") )"},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Difference test for two proportions — prop_diff_test","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. method (string) one chisq, cmh, fisher, schouten; specifies test used calculate p-value. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. var_labels (character) variable labels. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"test_proportion_diff\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Difference test for two proportions — prop_diff_test","text":"test_proportion_diff() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_test_proportion_diff() table layout. s_test_proportion_diff() returns named list single item pval attribute label describing method used. p-value tests null hypothesis proportions two groups . a_test_proportion_diff() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Difference test for two proportions — prop_diff_test","text":"test_proportion_diff(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_test_proportion_diff(): Statistics function tests difference two proportions. a_test_proportion_diff(): Formatted analysis function used afun test_proportion_diff().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/prop_diff_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Difference test for two proportions — prop_diff_test","text":"","code":"dta <- data.frame( rsp = sample(c(TRUE, FALSE), 100, TRUE), grp = factor(rep(c(\"A\", \"B\"), each = 50)), strata = factor(rep(c(\"V\", \"W\", \"X\", \"Y\", \"Z\"), each = 20)) ) # With `rtables` pipelines. l <- basic_table() %>% split_cols_by(var = \"grp\", ref_group = \"B\") %>% test_proportion_diff( vars = \"rsp\", method = \"cmh\", variables = list(strata = \"strata\") ) build_table(l, df = dta) #> A B #> ————————————————————————————————————————————————————— #> p-value (Cochran-Mantel-Haenszel Test) 1.0000"},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence table pruning — prune_occurrences","title":"Occurrence table pruning — prune_occurrences","text":"Family constructor condition functions flexibly prune occurrence tables. condition functions always return whether row result higher threshold. Since class CombinationFunction() can logically combined condition functions.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence table pruning — prune_occurrences","text":"","code":"keep_rows(row_condition) keep_content_rows(content_row_condition) has_count_in_cols(atleast, ...) has_count_in_any_col(atleast, ...) has_fraction_in_cols(atleast, ...) has_fraction_in_any_col(atleast, ...) has_fractions_difference(atleast, ...) has_counts_difference(atleast, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence table pruning — prune_occurrences","text":"row_condition (CombinationFunction) condition function works individual analysis rows flags whether kept pruned table. content_row_condition (CombinationFunction) condition function works individual first content rows leaf tables flags whether leaf tables kept pruned table. atleast (numeric(1)) threshold met order keep row. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence table pruning — prune_occurrences","text":"keep_rows() returns pruning function can used rtables::prune_table() prune rtables table. keep_content_rows() returns pruning function checks condition first content row leaf tables table. has_count_in_cols() returns condition function sums counts specified column. has_count_in_any_col() returns condition function compares counts specified columns threshold. has_fraction_in_cols() returns condition function sums counts specified column, computes fraction dividing total column counts. has_fraction_in_any_col() returns condition function looks fractions specified columns checks whether fulfill threshold. has_fractions_difference() returns condition function extracts fractions specified column, computes difference minimum maximum. has_counts_difference() returns condition function extracts counts specified column, computes difference minimum maximum.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence table pruning — prune_occurrences","text":"keep_rows(): Constructor creating pruning functions based row condition function. removes analysis rows (TableRow) pruned, .e., fulfill row condition. removes sub-tree children left. keep_content_rows(): Constructor creating pruning functions based condition (first) content row leaf tables. removes leaf tables first content row fulfill condition. check individual rows. proceeds recursively removing sub tree children left. has_count_in_cols(): Constructor creating condition functions total counts specified columns. has_count_in_any_col(): Constructor creating condition functions counts specified columns satisfying threshold. has_fraction_in_cols(): Constructor creating condition functions total fraction specified columns. has_fraction_in_any_col(): Constructor creating condition functions fraction specified columns. has_fractions_difference(): Constructor creating condition function checks difference fractions reported specified column. has_counts_difference(): Constructor creating condition function checks difference counts reported specified column.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Occurrence table pruning — prune_occurrences","text":"Since table specifications worded positively, name constructor condition functions positively, . However, note result keep_rows() says pruned, conform rtables::prune_table() interface.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/prune_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence table pruning — prune_occurrences","text":"","code":"# \\donttest{ tab <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze_vars(\"COUNTRY\", .stats = \"count_fraction\") %>% build_table(DM) # } # \\donttest{ # `keep_rows` is_non_empty <- !CombinationFunction(all_zero_or_na) prune_table(tab, keep_rows(is_non_empty)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # `keep_content_rows` # \\donttest{ more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab)) prune_table(tab, keep_content_rows(more_than_twenty)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> CHE 0 0 0 #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> CAN 0 0 0 #> CHE 0 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> GBR 0 0 0 #> CAN 0 0 0 #> CHE 0 0 0 #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> RUS 0 0 0 #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> CAN 0 0 0 #> CHE 0 0 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> NGA 0 0 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> CHE 0 0 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> JPN 0 0 0 #> GBR 0 0 0 #> CAN 0 0 0 #> CHE 0 0 0 # } # \\donttest{ more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_count_in_any_col` any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(any_more_than_one)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_fraction_in_cols` more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> BLACK OR AFRICAN AMERICAN #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) # } # \\donttest{ # `has_fraction_in_any_col` any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(any_atleast_five_percent)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ # `has_fractions_difference` more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab)) prune_table(tab, keep_rows(more_than_five_percent_diff)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> PAK 1 (8.3%) 1 (9.1%) 1 (14.3%) #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # } # \\donttest{ more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab)) prune_table(tab, keep_rows(more_than_one_diff)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> CHN 14 (51.9%) 9 (45%) 12 (38.7%) #> USA 2 (7.4%) 1 (5%) 8 (25.8%) #> BRA 1 (3.7%) 4 (20%) 1 (3.2%) #> PAK 3 (11.1%) 2 (10%) 2 (6.5%) #> NGA 3 (11.1%) 1 (5%) 3 (9.7%) #> RUS 2 (7.4%) 1 (5%) 1 (3.2%) #> JPN 0 1 (5%) 2 (6.5%) #> GBR 0 1 (5%) 1 (3.2%) #> CAN 2 (7.4%) 0 1 (3.2%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> CHN 12 (50%) 13 (44.8%) 11 (50%) #> USA 2 (8.3%) 5 (17.2%) 1 (4.5%) #> BRA 4 (16.7%) 3 (10.3%) 1 (4.5%) #> PAK 2 (8.3%) 2 (6.9%) 4 (18.2%) #> NGA 2 (8.3%) 1 (3.4%) 3 (13.6%) #> RUS 1 (4.2%) 1 (3.4%) 2 (9.1%) #> JPN 0 4 (13.8%) 0 #> GBR 1 (4.2%) 0 0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> CHN 13 (46.4%) 10 (52.6%) 16 (51.6%) #> USA 3 (10.7%) 3 (15.8%) 4 (12.9%) #> BRA 1 (3.6%) 1 (5.3%) 4 (12.9%) #> PAK 1 (3.6%) 1 (5.3%) 3 (9.7%) #> NGA 4 (14.3%) 1 (5.3%) 2 (6.5%) #> RUS 4 (14.3%) 2 (10.5%) 1 (3.2%) #> JPN 2 (7.1%) 1 (5.3%) 1 (3.2%) #> BLACK OR AFRICAN AMERICAN #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> CHN 3 (50%) 3 (42.9%) 5 (62.5%) #> USA 2 (33.3%) 1 (14.3%) 1 (12.5%) #> BRA 0 1 (14.3%) 0 #> PAK 0 0 1 (12.5%) #> NGA 0 0 1 (12.5%) #> JPN 1 (16.7%) 0 0 #> GBR 0 2 (28.6%) 0 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> CHN 3 (30%) 2 (33.3%) 8 (66.7%) #> USA 1 (10%) 0 1 (8.3%) #> BRA 2 (20%) 1 (16.7%) 1 (8.3%) #> PAK 0 1 (16.7%) 0 #> RUS 0 1 (16.7%) 0 #> JPN 2 (20%) 0 1 (8.3%) #> GBR 1 (10%) 0 1 (8.3%) #> CAN 1 (10%) 1 (16.7%) 0 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> CHN 8 (66.7%) 5 (45.5%) 5 (71.4%) #> USA 1 (8.3%) 2 (18.2%) 1 (14.3%) #> BRA 1 (8.3%) 2 (18.2%) 0 #> NGA 0 1 (9.1%) 0 #> RUS 1 (8.3%) 0 0 #> WHITE #> A 3 (2.5%) 6 (5.7%) 6 (4.7%) #> CHN 2 (66.7%) 2 (33.3%) 3 (50%) #> USA 1 (33.3%) 1 (16.7%) 0 #> PAK 0 1 (16.7%) 1 (16.7%) #> NGA 0 1 (16.7%) 0 #> RUS 0 0 1 (16.7%) #> JPN 0 1 (16.7%) 0 #> CAN 0 0 1 (16.7%) #> B 7 (5.8%) 5 (4.7%) 4 (3.1%) #> CHN 4 (57.1%) 1 (20%) 3 (75%) #> USA 0 1 (20%) 0 #> BRA 0 1 (20%) 0 #> PAK 1 (14.3%) 0 0 #> NGA 1 (14.3%) 0 0 #> RUS 1 (14.3%) 0 1 (25%) #> JPN 0 1 (20%) 0 #> CAN 0 1 (20%) 0 #> C 4 (3.3%) 3 (2.8%) 8 (6.2%) #> CHN 3 (75%) 3 (100%) 6 (75%) #> USA 1 (25%) 0 1 (12.5%) #> JPN 0 0 1 (12.5%) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":null,"dir":"Reference","previous_headings":"","what":"Re-implemented range() default S3 method for numerical objects — range_noinf","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"function returns c(NA, NA) instead c(-Inf, Inf) zero-length data without warnings.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"","code":"range_noinf(x, na.rm = FALSE, finite = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"x (numeric) sequence numbers range computed. na.rm (flag) flag indicating NA omitted. finite (flag) flag indicating non-finite elements removed.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/range_noinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Re-implemented range() default S3 method for numerical objects — range_noinf","text":"2-element vector class numeric.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":null,"dir":"Reference","previous_headings":"","what":"Reapply variable labels — reapply_varlabels","title":"Reapply variable labels — reapply_varlabels","text":"helper function used tests.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reapply variable labels — reapply_varlabels","text":"","code":"reapply_varlabels(x, varlabels, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reapply variable labels — reapply_varlabels","text":"x (vector) vector elements needs new labels. varlabels (character) vector labels x. ... parameters added list.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/reapply_varlabels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reapply variable labels — reapply_varlabels","text":"x variable labels reapplied.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"tabulate_rsp_biomarkers() function creates layout element tabulate estimated biomarker effects binary response endpoint across subgroups, returning statistics including response rate odds ratio population subgroup. table created df, list data frames returned extract_rsp_biomarkers(), statistics include specified via vars parameter. forest plot can created resulting table using g_forest() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"","code":"tabulate_rsp_biomarkers( df, vars = c(\"n_tot\", \"n_rsp\", \"prop\", \"or\", \"ci\", \"pval\"), na_str = default_na_str(), .indent_mods = 0L )"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"df (data.frame) containing analysis variables, returned extract_rsp_biomarkers(). vars (character) names statistics reported among: n_tot: Total number patients per group. n_rsp: Total number responses per group. prop: Total response proportion per group. : Odds ratio. ci: Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, ci required. na_str (string) string used replace NA empty values output. .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"rtables table summarizing biomarker effects binary response subgroup.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"functions create layout starting data frame contains required statistics. tables typically used input forest plots.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"contrast tabulate_rsp_subgroups() tabulation function start input layout lyt. internally table created combining multiple subtables.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/response_biomarkers_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate biomarker effects on binary response by subgroup — response_biomarkers_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate(rsp = AVALC == \"CR\") formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") df <- extract_rsp_biomarkers( variables = list( rsp = \"rsp\", biomarkers = c(\"BMRKR1\", \"AGE\"), covariates = \"SEX\", subgroups = \"BMRKR2\" ), data = adrs_f ) # \\donttest{ ## Table with default columns. tabulate_rsp_biomarkers(df) #> Total n Responders Response (%) Odds Ratio 95% CI p-value (Wald) #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Age #> All Patients 200 164 82.0% 1.00 (0.95, 1.05) 0.8530 #> Continuous Level Biomarker 2 #> LOW 70 53 75.7% 0.93 (0.85, 1.01) 0.0845 #> MEDIUM 68 58 85.3% 0.99 (0.88, 1.11) 0.8190 #> HIGH 62 53 85.5% 1.06 (0.96, 1.18) 0.2419 #> Continuous Level Biomarker 1 #> All Patients 200 164 82.0% 0.98 (0.88, 1.08) 0.6353 #> Continuous Level Biomarker 2 #> LOW 70 53 75.7% 1.15 (0.95, 1.40) 0.1584 #> MEDIUM 68 58 85.3% 0.88 (0.73, 1.06) 0.1700 #> HIGH 62 53 85.5% 0.88 (0.72, 1.08) 0.2104 ## Table with a manually chosen set of columns: leave out \"pval\", reorder. tab <- tabulate_rsp_biomarkers( df = df, vars = c(\"n_rsp\", \"ci\", \"n_tot\", \"prop\", \"or\") ) ## Finally produce the forest plot. g_forest(tab, xlim = c(0.7, 1.4)) # }"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate binary response by subgroup — response_subgroups","title":"Tabulate binary response by subgroup — response_subgroups","text":"tabulate_rsp_subgroups() function creates layout element tabulate binary response subgroup, returning statistics including response rate odds ratio population subgroup. table created df, list data frames returned extract_rsp_subgroups(), statistics include specified via vars parameter. forest plot can created resulting table using g_forest() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate binary response by subgroup — response_subgroups","text":"","code":"tabulate_rsp_subgroups( lyt, df, vars = c(\"n_tot\", \"n\", \"prop\", \"or\", \"ci\"), groups_lists = list(), label_all = \"All Patients\", riskdiff = NULL, na_str = default_na_str(), .formats = c(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\") ) a_response_subgroups( .formats = list(n = \"xx\", n_rsp = \"xx\", prop = \"xx.x%\", n_tot = \"xx\", or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)), pval = \"x.xxxx | (<0.0001)\", riskdiff = \"xx.x (xx.x - xx.x)\"), na_str = default_na_str() )"},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate binary response by subgroup — response_subgroups","text":"lyt (PreDataTableLayouts) layout analyses added . df (list) list data frames containing analysis variables. List created using extract_rsp_subgroups(). vars (character) names statistics reported among: n: Total number observations per group. n_rsp: Number responders per group. prop: Proportion responders. n_tot: Total number observations. : Odds ratio. ci : Confidence interval odds ratio. pval: p-value effect. Note, statistics n_tot, , ci required. groups_lists (named list list) optionally contains subgroups variable list, specifies new group levels via names levels belong character vectors elements list. label_all (string) label total population analysis. riskdiff (list) risk (proportion) difference column added, list settings apply within column. See control_riskdiff() details. NULL, risk difference column added. riskdiff$arm_x riskdiff$arm_y NULL, first level df$prop$arm used arm_x second level arm_y. na_str (string) string used replace NA empty values output. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate binary response by subgroup — response_subgroups","text":"rtables table summarizing binary response subgroup. a_response_subgroups() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate binary response by subgroup — response_subgroups","text":"functions create layout starting data frame contains required statistics. Tables typically used part forest plot.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Tabulate binary response by subgroup — response_subgroups","text":"tabulate_rsp_subgroups(): Table-creating function creates table summarizing binary response subgroup. function wrapper rtables::analyze_colvars() rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function used afun tabulate_rsp_subgroups().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/response_subgroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate binary response by subgroup — response_subgroups","text":"","code":"library(dplyr) library(forcats) adrs <- tern_ex_adrs adrs_labels <- formatters::var_labels(adrs) adrs_f <- adrs %>% filter(PARAMCD == \"BESRSPI\") %>% filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% droplevels() %>% mutate( # Reorder levels of factor to make the placebo group the reference arm. ARM = fct_relevel(ARM, \"B: Placebo\"), rsp = AVALC == \"CR\" ) formatters::var_labels(adrs_f) <- c(adrs_labels, \"Response\") # Unstratified analysis. df <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f ) df #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.1804488 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.2669576 51.452218 0.95 F SEX #> 3 64 0.920000 0.2966470 2.853223 0.95 M SEX #> 4 50 3.553846 1.0047370 12.570277 0.95 LOW BMRKR2 #> 5 49 2.705882 0.5911718 12.385232 0.95 MEDIUM BMRKR2 #> 6 43 2.250000 0.3970298 12.750933 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Stratified analysis. df_strat <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\"), strata = \"STRATA1\"), data = adrs_f ) df_strat #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 LOW BMRKR2 #> 8 A: Drug X 26 21 0.8076923 LOW BMRKR2 #> 9 B: Placebo 23 17 0.7391304 MEDIUM BMRKR2 #> 10 A: Drug X 26 23 0.8846154 MEDIUM BMRKR2 #> 11 B: Placebo 26 20 0.7692308 HIGH BMRKR2 #> 12 A: Drug X 17 15 0.8823529 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.6343899 1.1537821 6.015009 0.95 All Patients ALL #> 2 78 9.5946605 2.0379337 45.171985 0.95 F SEX #> 3 64 0.8947158 0.2936803 2.725809 0.95 M SEX #> 4 50 3.5976656 1.0101319 12.813374 0.95 LOW BMRKR2 #> 5 49 2.6242168 0.5162572 13.339308 0.95 MEDIUM BMRKR2 #> 6 43 2.2816865 0.4116391 12.647228 0.95 HIGH BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Grouping of the BMRKR2 levels. df_grouped <- extract_rsp_subgroups( variables = list(rsp = \"rsp\", arm = \"ARM\", subgroups = c(\"SEX\", \"BMRKR2\")), data = adrs_f, groups_lists = list( BMRKR2 = list( \"low\" = \"LOW\", \"low/medium\" = c(\"LOW\", \"MEDIUM\"), \"low/medium/high\" = c(\"LOW\", \"MEDIUM\", \"HIGH\") ) ) ) df_grouped #> $prop #> arm n n_rsp prop subgroup var #> 1 B: Placebo 73 50 0.6849315 All Patients ALL #> 2 A: Drug X 69 59 0.8550725 All Patients ALL #> 3 B: Placebo 40 25 0.6250000 F SEX #> 4 A: Drug X 38 36 0.9473684 F SEX #> 5 B: Placebo 33 25 0.7575758 M SEX #> 6 A: Drug X 31 23 0.7419355 M SEX #> 7 B: Placebo 24 13 0.5416667 low BMRKR2 #> 8 A: Drug X 26 21 0.8076923 low BMRKR2 #> 9 B: Placebo 47 30 0.6382979 low/medium BMRKR2 #> 10 A: Drug X 52 44 0.8461538 low/medium BMRKR2 #> 11 B: Placebo 73 50 0.6849315 low/medium/high BMRKR2 #> 12 A: Drug X 69 59 0.8550725 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 All Patients content #> 3 Sex analysis #> 4 Sex analysis #> 5 Sex analysis #> 6 Sex analysis #> 7 Continuous Level Biomarker 2 analysis #> 8 Continuous Level Biomarker 2 analysis #> 9 Continuous Level Biomarker 2 analysis #> 10 Continuous Level Biomarker 2 analysis #> 11 Continuous Level Biomarker 2 analysis #> 12 Continuous Level Biomarker 2 analysis #> #> $or #> arm n_tot or lcl ucl conf_level subgroup var #> 1 142 2.714000 1.180449 6.239827 0.95 All Patients ALL #> 2 78 10.800000 2.266958 51.452218 0.95 F SEX #> 3 64 0.920000 0.296647 2.853223 0.95 M SEX #> 4 50 3.553846 1.004737 12.570277 0.95 low BMRKR2 #> 5 99 3.116667 1.193409 8.139385 0.95 low/medium BMRKR2 #> 6 142 2.714000 1.180449 6.239827 0.95 low/medium/high BMRKR2 #> var_label row_type #> 1 All Patients content #> 2 Sex analysis #> 3 Sex analysis #> 4 Continuous Level Biomarker 2 analysis #> 5 Continuous Level Biomarker 2 analysis #> 6 Continuous Level Biomarker 2 analysis #> # Table with default columns basic_table() %>% tabulate_rsp_subgroups(df) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24) #> Sex #> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45) #> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85) #> Continuous Level Biomarker 2 #> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57) #> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39) #> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75) # Table with selected columns basic_table() %>% tabulate_rsp_subgroups( df = df, vars = c(\"n_tot\", \"n\", \"n_rsp\", \"prop\", \"or\", \"ci\") ) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Responders Response (%) n Responders Response (%) Odds Ratio 95% CI #> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 50 68.5% 69 59 85.5% 2.71 (1.18, 6.24) #> Sex #> F 78 40 25 62.5% 38 36 94.7% 10.80 (2.27, 51.45) #> M 64 33 25 75.8% 31 23 74.2% 0.92 (0.30, 2.85) #> Continuous Level Biomarker 2 #> LOW 50 24 13 54.2% 26 21 80.8% 3.55 (1.00, 12.57) #> MEDIUM 49 23 17 73.9% 26 23 88.5% 2.71 (0.59, 12.39) #> HIGH 43 26 20 76.9% 17 15 88.2% 2.25 (0.40, 12.75) # Table with risk difference column added basic_table() %>% tabulate_rsp_subgroups( df, riskdiff = control_riskdiff( arm_x = levels(df$prop$arm)[1], arm_y = levels(df$prop$arm)[2] ) ) #> B: Placebo A: Drug X #> Baseline Risk Factors Total n n Response (%) n Response (%) Odds Ratio 95% CI Risk Difference (%) (95% CI) #> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> All Patients 142 73 68.5% 69 85.5% 2.71 (1.18, 6.24) -17.0 (-30.5 - -3.5) #> Sex #> F 78 40 62.5% 38 94.7% 10.80 (2.27, 51.45) -32.2 (-48.8 - -15.6) #> M 64 33 75.8% 31 74.2% 0.92 (0.30, 2.85) 1.6 (-19.7 - 22.8) #> Continuous Level Biomarker 2 #> LOW 50 24 54.2% 26 80.8% 3.55 (1.00, 12.57) -26.6 (-51.6 - -1.6) #> MEDIUM 49 23 73.9% 26 88.5% 2.71 (0.59, 12.39) -14.5 (-36.3 - 7.2) #> HIGH 43 26 76.9% 17 88.2% 2.25 (0.40, 12.75) -11.3 (-33.6 - 11.0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert rtable objects to ggplot objects — rtable2gg","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"Given rtables::rtable() object, performs basic conversion ggplot2::ggplot() object built using functions ggplot2 package. table titles /footnotes ignored.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"","code":"rtable2gg(tbl, fontsize = 12, colwidths = NULL, lbl_col_padding = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"tbl (VTableTree)rtables table object. fontsize (numeric(1)) font size. colwidths (numeric NULL) vector column widths. element's position colwidths corresponds column tbl position. NULL, column widths calculated according maximum number characters per column. lbl_col_padding (numeric) additional padding use calculating spacing first (label) column second column tbl. colwidths specified, width first column becomes colwidths[1] + lbl_col_padding. Defaults 0.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"ggplot object.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtable2gg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert rtable objects to ggplot objects — rtable2gg","text":"","code":"dta <- data.frame( ARM = rep(LETTERS[1:3], rep(6, 3)), AVISIT = rep(paste0(\"V\", 1:3), 6), AVAL = c(9:1, rep(NA, 9)) ) lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(var = \"AVISIT\") %>% analyze_vars(vars = \"AVAL\") tbl <- build_table(lyt, df = dta) rtable2gg(tbl) rtable2gg(tbl, fontsize = 15, colwidths = c(2, 1, 1, 1))"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper functions for accessing information from rtables — rtables_access","title":"Helper functions for accessing information from rtables — rtables_access","text":"couple functions help accessing data rtables objects. Currently work occurrence tables, defined count first element fraction second element cell.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper functions for accessing information from rtables — rtables_access","text":"","code":"h_row_first_values(table_row, col_names = NULL, col_indices = NULL) h_row_counts(table_row, col_names = NULL, col_indices = NULL) h_row_fractions(table_row, col_names = NULL, col_indices = NULL) h_col_counts(table, col_names = NULL, col_indices = NULL) h_content_first_row(table) is_leaf_table(table) check_names_indices(table_row, col_names = NULL, col_indices = NULL)"},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper functions for accessing information from rtables — rtables_access","text":"table_row (TableRow) analysis row occurrence table. col_names (character) names columns extract . col_indices (integer) indices columns extract . col_names provided, inferred names table_row. Note currently works well single column split. table (VTableNodeInfo) occurrence table row.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper functions for accessing information from rtables — rtables_access","text":"h_row_first_values() returns vector numeric values. h_row_counts() returns vector numeric values. h_row_fractions() returns vector proportions. h_col_counts() returns vector column counts. h_content_first_row() returns row rtables table. is_leaf_table() returns logical value indicating whether current table leaf. check_names_indices returns column indices.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Helper functions for accessing information from rtables — rtables_access","text":"h_row_first_values(): Helper function extract first values content cell specified columns TableRow. Defaults columns. h_row_counts(): Helper function extracts row values checks convertible integers (integerish values). h_row_fractions(): Helper function extract fractions specified columns TableRow. specifically extracts second values content cell checks fraction. h_col_counts(): Helper function extract column counts specified columns table. h_content_first_row(): Helper function get first row content table current table. is_leaf_table(): Helper function says whether current table leaf tree. check_names_indices(): Internal helper function tests standard inputs column indices.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/rtables_access.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper functions for accessing information from rtables — rtables_access","text":"","code":"tbl <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% analyze(\"AGE\", function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.x (xx.x)\"), \"n\" = length(x), \"frac\" = rcell(c(0.1, 0.1), format = \"xx (xx)\") ) }) %>% build_table(tern_ex_adsl) %>% prune_table() tree_row_elem <- collect_leaves(tbl[2, ])[[1]] result <- max(h_row_first_values(tree_row_elem)) result #> [1] 35.31214 # Row counts (integer values) # h_row_counts(tree_row_elem) # Fails because there are no integers # Using values with integers tree_row_elem <- collect_leaves(tbl[3, ])[[1]] result <- h_row_counts(tree_row_elem) # result # Row fractions tree_row_elem <- collect_leaves(tbl[4, ])[[1]] h_row_fractions(tree_row_elem) #> A: Drug X B: Placebo C: Combination #> 0.1 0.1 0.1"},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Cox model - summarized results — s_cox_multivariate","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"Analyses based multivariate Cox model usually performed Controlled Substance Reporting regulatory documents serve exploratory purposes (e.g., publication). practice, model usually includes main effects (without interaction terms). produces hazard ratio estimates covariates included model. analysis follows principles (e.g., stratified vs. unstratified analysis tie handling) usual Cox model analysis. Since usually pre-specified hypothesis testing analysis, p.values need interpreted caution. (Statistical Analysis Clinical Trials Data R, NEST's bookdown)","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"","code":"s_cox_multivariate( formula, data, conf_level = 0.95, pval_method = c(\"wald\", \"likelihood\"), ... )"},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"formula (formula) formula corresponding investigated survival::Surv() survival model including covariates. data (data.frame) data frame includes variable formula covariates. conf_level (proportion) confidence level hazard ratio interval estimations. Default 0.95. pval_method (string) method used estimation p-values, one \"wald\" (default) \"likelihood\". ... optional parameters passed survival::coxph(). Can include ties, character string specifying method tie handling, one exact (default), efron, breslow.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"list elements mod, msum, aov, coef_inter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"output limited single effect terms. Work ongoing estimation interaction terms scope defined Global Data Standards Repository (GDS_Standard_TLG_Specs_Tables_2.doc).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/s_cox_multivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Cox model - summarized results — s_cox_multivariate","text":"","code":"library(dplyr) adtte <- tern_ex_adtte adtte_f <- subset(adtte, PARAMCD == \"OS\") # _f: filtered adtte_f <- filter( adtte_f, PARAMCD == \"OS\" & SEX %in% c(\"F\", \"M\") & RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\") ) adtte_f$SEX <- droplevels(adtte_f$SEX) adtte_f$RACE <- droplevels(adtte_f$RACE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert strings to NA — sas_na","title":"Convert strings to NA — sas_na","text":"SAS imports missing data empty strings strings whitespaces . helper function can used convert values NAs.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert strings to NA — sas_na","text":"","code":"sas_na(x, empty = TRUE, whitespaces = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert strings to NA — sas_na","text":"x (factor character) values missing values substituted. empty (flag) TRUE, empty strings get replaced NA. whitespaces (flag) TRUE, strings made whitespaces get replaced NA.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert strings to NA — sas_na","text":"x \"\" /whitespace-values substituted NA, depending values empty whitespaces.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/sas_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert strings to NA — sas_na","text":"","code":"sas_na(c(\"1\", \"\", \" \", \" \", \"b\")) #> [1] \"1\" NA NA NA \"b\" sas_na(factor(c(\"\", \" \", \"b\"))) #> [1] b #> Levels: b is.na(sas_na(c(\"1\", \"\", \" \", \" \", \"b\"))) #> [1] FALSE TRUE TRUE TRUE FALSE"},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":null,"dir":"Reference","previous_headings":"","what":"Occurrence table sorting — score_occurrences","title":"Occurrence table sorting — score_occurrences","text":"Functions score occurrence table subtables rows can used sorting occurrence tables.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Occurrence table sorting — score_occurrences","text":"","code":"score_occurrences(table_row) score_occurrences_cols(...) score_occurrences_subtable(...) score_occurrences_cont_cols(...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Occurrence table sorting — score_occurrences","text":"table_row (TableRow) analysis row occurrence table. ... arguments row column access, see rtables_access: either col_names (character) including names columns used, alternatively col_indices (integer) giving indices directly instead.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Occurrence table sorting — score_occurrences","text":"score_occurrences() returns sum counts across columns table row. score_occurrences_cols() returns function sums counts across specified columns table row. score_occurrences_subtable() returns function sums counts subtable across specified columns. score_occurrences_cont_cols() returns function sums counts first content row specified columns.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Occurrence table sorting — score_occurrences","text":"score_occurrences(): Scoring function sums counts across columns. fail anything else counts used. score_occurrences_cols(): Scoring functions can produced constructor include specific columns scoring. See h_row_counts() information. score_occurrences_subtable(): Scoring functions produced constructor can used subtables: sum specified column counts subtable. useful available content row summing counts. score_occurrences_cont_cols(): Produces score function sorting table summing first content row specified columns. Note extending rtables::cont_n_onecol() rtables::cont_n_allcols().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/score_occurrences.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Occurrence table sorting — score_occurrences","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% analyze_num_patients( vars = \"USUBJID\", .stats = c(\"unique\"), .labels = c(\"Total number of patients with at least one event\") ) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>% summarize_num_patients( var = \"USUBJID\", .stats = c(\"unique\", \"nonunique\"), .labels = c( \"Total number of patients with at least one event\", \"Total number of events\" ) ) %>% count_occurrences(vars = \"AEDECOD\") tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>% prune_table() tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_occurrences) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%) score_cols_a_and_b <- score_occurrences_cols(col_names = c(\"A: Drug X\", \"B: Placebo\")) # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted. # That would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\", \"*\", \"AEDECOD\"), scorefun = score_cols_a_and_b) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%) score_subtable_all <- score_occurrences_subtable(col_names = names(tbl)) # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That # would require a second pass of `sort_at_path`. tbl_sorted <- tbl %>% sort_at_path(path = c(\"AEBODSYS\"), scorefun = score_subtable_all, decreasing = FALSE) tbl_sorted #> A: Drug X B: Placebo C: Combination #> (N=69) (N=73) (N=58) #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%) #> cl C.2 #> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%) #> Total number of events 21 15 14 #> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%) #> cl C.1 #> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%) #> Total number of events 17 15 19 #> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%) #> cl B.1 #> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%) #> Total number of events 19 19 18 #> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%) #> cl D.2 #> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%) #> Total number of events 27 22 15 #> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%) #> cl D.1 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%) #> Total number of events 39 35 28 #> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%) #> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%) #> cl B.2 #> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%) #> Total number of events 40 38 33 #> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%) #> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%) #> cl A.1 #> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%) #> Total number of events 39 33 35 #> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%) #> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%)"},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split columns by groups of levels — split_cols_by_groups","text":"","code":"split_cols_by_groups(lyt, var, groups_list = NULL, ref_group = NULL, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split columns by groups of levels — split_cols_by_groups","text":"lyt (PreDataTableLayouts) layout analyses added . var (string) single variable name passed rtables requested statistics function. groups_list (named list character) specifies new group levels via names levels belong character vectors elements list. ref_group (data.frame vector) data corresponding reference group. ... additional arguments rtables::split_cols_by() order. instance, control formats (format), add joint column groups (incl_all).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split columns by groups of levels — split_cols_by_groups","text":"layout object suitable passing layouting functions. Adding function rtable layout add column split including given groups table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/split_cols_by_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split columns by groups of levels — split_cols_by_groups","text":"","code":"# 1 - Basic use # Without group combination `split_cols_by_groups` is # equivalent to [rtables::split_cols_by()]. basic_table() %>% split_cols_by_groups(\"ARM\") %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> —————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 # Add a reference column. basic_table() %>% split_cols_by_groups(\"ARM\", ref_group = \"B: Placebo\") %>% add_colcounts() %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff Mean\" = rcell(NULL)) } else { in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ——————————————————————————————————————————————————— #> Diff Mean 1.89 1.55 # 2 - Adding group specification # Manual preparation of the groups. groups <- list( \"Arms A+B\" = c(\"A: Drug X\", \"B: Placebo\"), \"Arms A+C\" = c(\"A: Drug X\", \"C: Combination\") ) # Use of split_cols_by_groups without reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups) %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> Arms A+B Arms A+C #> (N=227) (N=250) #> —————————————————————————— #> Mean 34.03 34.73 # Including differentiated output in the reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups_list = groups, ref_group = \"Arms A+B\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff. of Averages\" = rcell(NULL)) } else { in_rows(\"Diff. of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> Arms A+B Arms A+C #> ——————————————————————————————————————— #> Diff. of Averages 0.71 # 3 - Binary list dividing factor levels into reference and treatment # `combine_groups` defines reference and treatment. groups <- combine_groups( fct = DM$ARM, ref = c(\"A: Drug X\", \"B: Placebo\") ) groups #> $`A: Drug X/B: Placebo` #> [1] \"A: Drug X\" \"B: Placebo\" #> #> $`C: Combination` #> [1] \"C: Combination\" #> # Use group definition without reference column. basic_table() %>% split_cols_by_groups(\"ARM\", groups_list = groups) %>% add_colcounts() %>% analyze(\"AGE\") %>% build_table(DM) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ———————————————————————————————————————————— #> Mean 34.03 34.57 # Use group definition with reference column (first item of groups). basic_table() %>% split_cols_by_groups(\"ARM\", groups, ref_group = names(groups)[1]) %>% add_colcounts() %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { in_rows(\"Diff Mean\" = rcell(NULL)) } else { in_rows(\"Diff Mean\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\")) } } ) %>% build_table(DM) #> A: Drug X/B: Placebo C: Combination #> (N=227) (N=129) #> ————————————————————————————————————————————————— #> Diff Mean 0.54"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":null,"dir":"Reference","previous_headings":"","what":"Split text according to available text width — split_text_grob","title":"Split text according to available text width — split_text_grob","text":"Dynamically wrap text.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split text according to available text width — split_text_grob","text":"","code":"split_text_grob( text, x = grid::unit(0.5, \"npc\"), y = grid::unit(0.5, \"npc\"), width = grid::unit(1, \"npc\"), just = \"centre\", hjust = NULL, vjust = NULL, default.units = \"npc\", name = NULL, gp = grid::gpar(), vp = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split text according to available text width — split_text_grob","text":"text (string) text wrap. x numeric vector unit object specifying x-values. y numeric vector unit object specifying y-values. width (grid::unit) unit object specifying maximum width text. just justification text relative (x, y) location. two values, first value specifies horizontal justification second value specifies vertical justification. Possible string values : \"left\", \"right\", \"centre\", \"center\", \"bottom\", \"top\". numeric values, 0 means left (bottom) alignment 1 means right (top) alignment. hjust numeric vector specifying horizontal justification. specified, overrides just setting. vjust numeric vector specifying vertical justification. specified, overrides just setting. default.units string indicating default units use x y given numeric vectors. name character identifier. gp object class \"gpar\", typically output call function gpar. basically list graphical parameter settings. vp Grid viewport object (NULL).","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split text according to available text width — split_text_grob","text":"text grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/split_text_grob.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split text according to available text width — split_text_grob","text":"code taken R Graphics Paul Murell, 2nd edition","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":null,"dir":"Reference","previous_headings":"","what":"Stack multiple grobs — stack_grobs","title":"Stack multiple grobs — stack_grobs","text":"Stack grobs new grob 1 column multiple rows layout.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stack multiple grobs — stack_grobs","text":"","code":"stack_grobs( ..., grobs = list(...), padding = grid::unit(2, \"line\"), vp = NULL, gp = NULL, name = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stack multiple grobs — stack_grobs","text":"... grobs. grobs (list grob) list grobs. padding (grid::unit) unit length 1, space grob. vp (viewport NULL) viewport() object (NULL). gp (gpar) gpar() object. name (string) character identifier grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stack multiple grobs — stack_grobs","text":"grob.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stack_grobs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stack multiple grobs — stack_grobs","text":"","code":"library(grid) g1 <- circleGrob(gp = gpar(col = \"blue\")) g2 <- circleGrob(gp = gpar(col = \"red\")) g3 <- textGrob(\"TEST TEXT\") grid.newpage() grid.draw(stack_grobs(g1, g2, g3)) showViewport() grid.newpage() pushViewport(viewport(layout = grid.layout(1, 2))) vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2) grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = \"test\")) showViewport() grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence interval for mean — stat_mean_ci","title":"Confidence interval for mean — stat_mean_ci","text":"Convenient function calculating mean confidence interval. calculates arithmetic well geometric mean. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence interval for mean — stat_mean_ci","text":"","code":"stat_mean_ci( x, conf_level = 0.95, na.rm = TRUE, n_min = 2, gg_helper = TRUE, geom_mean = FALSE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence interval for mean — stat_mean_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric(1)) minimum number non-missing x estimate confidence interval mean. gg_helper (flag) whether output aligned use ggplots. geom_mean (flag) whether geometric mean calculated.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence interval for mean — stat_mean_ci","text":"named vector values mean_ci_lwr mean_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence interval for mean — stat_mean_ci","text":"","code":"stat_mean_ci(sample(10), gg_helper = FALSE) #> mean_ci_lwr mean_ci_upr #> 3.334149 7.665851 p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) + ggplot2::geom_point() p + ggplot2::stat_summary( fun.data = stat_mean_ci, geom = \"errorbar\" ) p + ggplot2::stat_summary( fun.data = stat_mean_ci, fun.args = list(conf_level = 0.5), geom = \"errorbar\" ) p + ggplot2::stat_summary( fun.data = stat_mean_ci, fun.args = list(conf_level = 0.5, geom_mean = TRUE), geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":null,"dir":"Reference","previous_headings":"","what":"p-Value of the mean — stat_mean_pval","title":"p-Value of the mean — stat_mean_pval","text":"Convenient function calculating two-sided p-value mean.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"p-Value of the mean — stat_mean_pval","text":"","code":"stat_mean_pval(x, na.rm = TRUE, n_min = 2, test_mean = 0)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"p-Value of the mean — stat_mean_pval","text":"x (numeric) vector numbers want analyze. na.rm (flag) whether NA values removed x prior analysis. n_min (numeric(1)) minimum number non-missing x estimate p-value mean. test_mean (numeric(1)) mean value test null hypothesis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"p-Value of the mean — stat_mean_pval","text":"p-value.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_mean_pval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"p-Value of the mean — stat_mean_pval","text":"","code":"stat_mean_pval(sample(10)) #> p_value #> 0.000278196 stat_mean_pval(rnorm(10), test_mean = 0.5) #> p_value #> 0.04235675"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence interval for median — stat_median_ci","title":"Confidence interval for median — stat_median_ci","text":"Convenient function calculating median confidence interval. can used ggplot helper function plotting.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence interval for median — stat_median_ci","text":"","code":"stat_median_ci(x, conf_level = 0.95, na.rm = TRUE, gg_helper = TRUE)"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence interval for median — stat_median_ci","text":"x (numeric) vector numbers want analyze. conf_level (proportion) confidence level interval. na.rm (flag) whether NA values removed x prior analysis. gg_helper (flag) whether output aligned use ggplots.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence interval for median — stat_median_ci","text":"named vector values median_ci_lwr median_ci_upr.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence interval for median — stat_median_ci","text":"function adapted DescTools/versions/0.99.35/source","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_median_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence interval for median — stat_median_ci","text":"","code":"stat_median_ci(sample(10), gg_helper = FALSE) #> median_ci_lwr median_ci_upr #> 2 9 #> attr(,\"conf_level\") #> [1] 0.9785156 p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) + ggplot2::geom_point() p + ggplot2::stat_summary( fun.data = stat_median_ci, geom = \"errorbar\" )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportion difference and confidence interval — stat_propdiff_ci","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"Function calculating proportion (risk) difference confidence interval arm X (reference group) arm Y. Risk difference calculated subtracting cumulative incidence arm Y cumulative incidence arm X.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci( x, y, N_x, N_y, list_names = NULL, conf_level = 0.95, pct = TRUE )"},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"x (list integer) list number occurrences arm X (reference group). y (list integer) list number occurrences arm Y. Must equal length x. N_x (numeric(1)) total number records arm X. N_y (numeric(1)) total number records arm Y. list_names (character) names variable/level corresponding pair proportions x y. Must equal length x y. conf_level (proportion) confidence level interval. pct (flag) whether output returned percentages. Defaults TRUE.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"List proportion differences CIs corresponding pair number occurrences x y. list element consists 3 statistics: proportion difference, CI lower bound, CI upper bound.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/stat_propdiff_ci.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Proportion difference and confidence interval — stat_propdiff_ci","text":"","code":"stat_propdiff_ci( x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = \"x\", conf_level = 0.9 ) #> $x #> [1] 7.30000 -12.35184 26.95184 #> stat_propdiff_ci( x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE ) #> [[1]] #> [1] 0.0375000 -0.1060891 0.1810891 #> #> [[2]] #> [1] 0.07250000 -0.09220915 0.23720915 #> #> [[3]] #> [1] 0.0750000 -0.1231285 0.2731285 #>"},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"function wraps estimation stratified percentiles assume approximation large numbers. necessary case proportions strata unequal.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"","code":"strata_normal_quantile(vars, weights, conf_level)"},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"vars (character) variable names primary analysis variable iterated . weights (numeric NULL) weights level strata. NULL, estimated using iterative algorithm proposed Yan Su (2010) minimizes weighted squared length confidence interval. conf_level (proportion) confidence level interval.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"Stratified quantile.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/strata_normal_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function for the estimation of stratified quantiles — strata_normal_quantile","text":"","code":"strata_data <- table(data.frame( \"f1\" = sample(c(TRUE, FALSE), 100, TRUE), \"f2\" = sample(c(\"x\", \"y\", \"z\"), 100, TRUE), stringsAsFactors = TRUE )) ns <- colSums(strata_data) ests <- strata_data[\"TRUE\", ] / ns vars <- ests * (1 - ests) / ns weights <- rep(1 / length(ns), length(ns)) strata_normal_quantile(vars, weights, 0.95) #> [1] 1.14181"},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":null,"dir":"Reference","previous_headings":"","what":"Indicate study arm variable in formula — study_arm","title":"Indicate study arm variable in formula — study_arm","text":"use study_arm indicate study arm variable tern formulas.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indicate study arm variable in formula — study_arm","text":"","code":"study_arm(x)"},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indicate study arm variable in formula — study_arm","text":"x arm information","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/study_arm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indicate study arm variable in formula — study_arm","text":"x","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"analyze function summarize_ancova() creates layout element summarize ANCOVA results. function can used analyze multiple endpoints /multiple timepoints within response variable(s) specified vars. Additional variables analysis, namely arm (grouping) variable covariate variables, can defined via variables argument. See details specify variables. interaction term can implemented model needed. interaction variable interact arm variable specified via interaction_term parameter, specific value interaction_term extract ANCOVA results via interaction_y parameter.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"","code":"summarize_ancova( lyt, vars, variables, conf_level, interaction_y = FALSE, interaction_item = NULL, var_labels, na_str = default_na_str(), nested = TRUE, ..., show_labels = \"visible\", table_names = vars, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_ancova( df, .var, .df_row, variables, .ref_group, .in_ref_col, conf_level, interaction_y = FALSE, interaction_item = NULL ) a_ancova( df, .var, .df_row, variables, .ref_group, .in_ref_col, conf_level, interaction_y = FALSE, interaction_item = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables, expected elements: arm (string) group variable, covariate adjusted means multiple groups summarized. Specifically, first level arm variable taken reference group. covariates (character) vector can contain single variable names (\"X1\"), /interaction terms indicated \"X1 * X2\". conf_level (proportion) confidence level interval. interaction_y (string flag) selected item inside interaction_item variable used select specific ANCOVA results. interaction needed, default option FALSE. interaction_item (string NULL) name variable interactions arm. interaction needed, default option NULL. var_labels (character) variable labels. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. show_labels (string) label visibility: one \"default\", \"visible\" \"hidden\". table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"summarize_ancova\") see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. .df_row (data.frame) data set includes variables called .var variables. .ref_group (data.frame vector) data corresponding reference group. .in_ref_col (flag)TRUE working reference level, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"summarize_ancova() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_ancova() table layout. s_ancova() returns named list 5 statistics: n: Count complete sample size group. lsmean: Estimated marginal means group. lsmean_diff: Difference estimated marginal means comparison reference group. working reference group, empty. lsmean_diff_ci: Confidence level difference estimated marginal means comparison reference group. pval: p-value (adjusted multiple comparisons). a_ancova() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"summarize_ancova(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_ancova(): Statistics function produces named list results investigated linear model. a_ancova(): Formatted analysis function used afun summarize_ancova().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_ancova.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize analysis of covariance (ANCOVA) results — summarize_ancova","text":"","code":"basic_table() %>% split_cols_by(\"Species\", ref_group = \"setosa\") %>% add_colcounts() %>% summarize_ancova( vars = \"Petal.Length\", variables = list(arm = \"Species\", covariates = NULL), table_names = \"unadj\", conf_level = 0.95, var_labels = \"Unadjusted comparison\", .labels = c(lsmean = \"Mean\", lsmean_diff = \"Difference in Means\") ) %>% summarize_ancova( vars = \"Petal.Length\", variables = list(arm = \"Species\", covariates = c(\"Sepal.Length\", \"Sepal.Width\")), table_names = \"adj\", conf_level = 0.95, var_labels = \"Adjusted comparison (covariates: Sepal.Length and Sepal.Width)\" ) %>% build_table(iris) #> setosa versicolor virginica #> (N=50) (N=50) (N=50) #> ————————————————————————————————————————————————————————————————————————————————————————————————————— #> Unadjusted comparison #> n 50 50 50 #> Mean 1.46 4.26 5.55 #> Difference in Means 2.80 4.09 #> 95% CI (2.63, 2.97) (3.92, 4.26) #> p-value <0.0001 <0.0001 #> Adjusted comparison (covariates: Sepal.Length and Sepal.Width) #> n 50 50 50 #> Adjusted Mean 2.02 4.19 5.07 #> Difference in Adjusted Means 2.17 3.05 #> 95% CI (1.96, 2.38) (2.81, 3.29) #> p-value <0.0001 <0.0001"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize change from baseline values or absolute baseline values — summarize_change","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"analyze function summarize_change() creates layout element summarize change baseline absolute baseline values. primary analysis variable vars indicates numerical change baseline results. Required secondary analysis variables value baseline_flag can supplied function via variables argument. value element name analysis value variable, baseline_flag element name flag variable indicates whether records contain baseline values. Depending baseline flag given, either absolute baseline values (baseline) change baseline values (post-baseline) summarized.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"","code":"summarize_change( lyt, vars, variables, na_str = default_na_str(), nested = TRUE, ..., table_names = vars, .stats = c(\"n\", \"mean_sd\", \"median\", \"range\"), .formats = NULL, .labels = NULL, .indent_mods = NULL ) s_change_from_baseline(df, .var, variables, na.rm = TRUE, ...) a_change_from_baseline(df, .var, variables, na.rm = TRUE, ...)"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"lyt (PreDataTableLayouts) layout analyses added . vars (character) variable names primary analysis variable iterated . variables (named list string) list additional analysis variables. na_str (string) string used replace NA empty values output. nested (flag) whether layout instruction applied within existing layout structure _if possible (TRUE, default) new top-level element (FALSE). Ignored nest split. underneath analyses, allowed. ... additional arguments lower level functions. table_names (character) can customized case vars analyzed multiple times, avoid warnings rtables. .stats (character) statistics select table. Run get_stats(\"analyze_vars_numeric) see available statistics function. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named integer) indent modifiers labels. Defaults 0, corresponds unmodified default behavior. Can negative. df (data.frame) data set containing analysis variables. .var (string) single variable name passed rtables requested statistics function. na.rm (flag) whether NA values removed x prior analysis.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"summarize_change() returns layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout add formatted rows containing statistics s_change_from_baseline() table layout. s_change_from_baseline() returns values returned s_summary.numeric(). a_change_from_baseline() returns corresponding list formatted rtables::CellValue().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"summarize_change(): Layout-creating function can take statistics function arguments additional format arguments. function wrapper rtables::analyze(). s_change_from_baseline(): Statistics function summarizes baseline post-baseline visits. a_change_from_baseline(): Formatted analysis function used afun summarize_change().","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"used split visits layout, data subset contains either baseline post-baseline data. data df must either baseline post-baseline visits. Otherwise error thrown.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize change from baseline values or absolute baseline values — summarize_change","text":"","code":"library(dplyr) ## Fabricate dataset dta_test <- data.frame( USUBJID = rep(1:6, each = 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)) ) %>% mutate(ABLFLL = AVISIT == \"V1\") %>% group_by(USUBJID) %>% mutate( BLVAL = AVAL[ABLFLL], CHG = AVAL - BLVAL ) %>% ungroup() results <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% summarize_change(\"CHG\", variables = list(value = \"AVAL\", baseline_flag = \"ABLFLL\")) %>% build_table(dta_test) results #> A B C #> ——————————————————————————————————————————————————————————— #> V1 #> n 2 1 0 #> Mean (SD) 7.50 (2.12) 3.00 () #> Median 7.50 3.00 #> Min - Max 6.00 - 9.00 3.00 - 3.00 #> V2 #> n 2 1 0 #> Mean (SD) -1.00 (0.00) -1.00 () #> Median -1.00 -1.00 #> Min - Max -1.00 - -1.00 -1.00 - -1.00 #> V3 #> n 2 1 0 #> Mean (SD) -2.00 (0.00) -2.00 () #> Median -2.00 -2.00 #> Min - Max -2.00 - -2.00 -2.00 - -2.00 "},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize variables in columns — summarize_colvars","title":"Summarize variables in columns — summarize_colvars","text":"analyze function summarize_colvars() uses statistics function s_summary() analyze variables arranged columns. variables analyze specified table layout via column splits (see split_cols_by() split_cols_by_multivar()) prior using summarize_colvars(). function minimal wrapper rtables::analyze_colvars(), function typically used apply different analysis methods rows column variable. use analysis methods column labels, please refer analyze_vars_in_cols() function.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize variables in columns — summarize_colvars","text":"","code":"summarize_colvars( lyt, ..., na_str = default_na_str(), .stats = c(\"n\", \"mean_sd\", \"median\", \"range\", \"count_fraction\"), .formats = NULL, .labels = NULL, .indent_mods = NULL )"},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize variables in columns — summarize_colvars","text":"lyt (PreDataTableLayouts) layout analyses added . ... arguments passed s_summary(). na_str (string) string used replace NA empty values output. .stats (character) statistics select table. .formats (named character list) formats statistics. See Details analyze_vars information \"auto\" setting. .labels (named character) labels statistics (without indent). .indent_mods (named vector integer) indent modifiers labels. element vector name-value pair name corresponding statistic specified .stats value indentation statistic's row label.","code":""},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize variables in columns — summarize_colvars","text":"layout object suitable passing layouting functions, rtables::build_table(). Adding function rtable layout summarize given variables, arrange output columns, add table layout.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/tern/main/reference/summarize_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize variables in columns — summarize_colvars","text":"","code":"dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)), CHG = c(1:9, rep(NA, 9)) ) ## Default output within a `rtables` pipeline. basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% summarize_colvars() %>% build_table(dta_test) #> A B C #> AVAL CHG AVAL CHG AVAL CHG #> ——————————————————————————————————————————————————————————————————————————————————————————————— #> V1 #> n 2 2 1 1 0 0 #> Mean (SD) 7.5 (2.1) 2.5 (2.1) 3.0 () 7.0 () #> Median 7.5 2.5 3.0 7.0 #> Min - Max 6.0 - 9.0 1.0 - 4.0 3.0 - 3.0 7.0 - 7.0 #> V2 #> n 2 2 1 1 0 0 #> Mean (SD) 6.5 (2.1) 3.5 (2.1) 2.0 () 8.0 () #> Median 6.5 3.5 2.0 8.0 #> Min - Max 5.0 - 8.0 2.0 - 5.0 2.0 - 2.0 8.0 - 8.0 #> V3 #> n 2 2 1 1 0 0 #> Mean (SD) 5.5 (2.1) 4.5 (2.1) 1.0 () 9.0 () #> Median 5.5 4.5 1.0 9.0 #> Min - Max 4.0 - 7.0 3.0 - 6.0 1.0 - 1.0 9.0 - 9.0
R
rtables
The packages used in this vignette are:
@@ -143,10 +113,10 @@ rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analyze functions are wrappers around -rtables::analyze function, they offer various methods +rtables::analyze function, they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analyze functions are @@ -173,26 +143,26 @@ Internals of tern Anal rcell type formatting themselves.
rtables::build_table
The tern analyze functions are wrappers around -rtables::analyze function, they offer various methods +rtables::analyze function, they offer various methods useful from the perspective of clinical trials and other statistical projects.
tern
rtables::analyze
Examples of the tern analyze functions are @@ -173,26 +143,26 @@
rcell
a_*
rtables::make_afun()
afun
rtables::analyze()
rtables::analyze(..., afun = make_afun(tern::a_*))
We will use the native rtables::analyze function with +
We will use the native rtables::analyze function with the tern formatted analysis functions as a afun parameter.
l <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = a_summary) +l <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = a_summary) -build_table(l, df = adrs) -The rtables::make_afun function is helpful when somebody +build_table(l, df = adrs)
l <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = a_summary) -build_table(l, df = adrs)
The rtables::make_afun function is helpful when somebody +build_table(l, df = adrs)
rtables::make_afun
The rtables::make_afun function is helpful when somebody wants to attach some format to the formatted analysis function.
afun <- make_afun( +afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = "xx."), @@ -200,12 +170,12 @@ Internals of tern Anal .indent_mods = c(median = 1L) ) -l2 <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = afun) +l2 <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = afun) -build_table(l2, df = adrs) +build_table(l2, df = adrs)
afun <- make_afun( a_summary, .stats = NULL, .formats = c(median = "xx."), @@ -200,12 +170,12 @@ Internals of tern Anal .indent_mods = c(median = 1L) ) -l2 <- basic_table() %>% - split_cols_by(var = "ARM") %>% - split_rows_by(var = "AVISIT") %>% - analyze(vars = "AVAL", afun = afun) +l2 <- basic_table() %>% + split_cols_by(var = "ARM") %>% + split_rows_by(var = "AVISIT") %>% + analyze(vars = "AVAL", afun = afun) -build_table(l2, df = adrs)
analyze_colvars()
summarize_row_groups()
analyze()
To customize the display for the demographics table, we can do so via the arguments in analyze_vars(). Most layout creating @@ -343,17 +313,17 @@
analyze_vars()
# Select statistics and modify default formats. -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels, .stats = c("n", "mean_sd", "count"), .formats = c(mean_sd = "xx.xx (xx.xx)") ) %>% - build_table(adsl) + build_table(adsl) #> B: Placebo A: Drug X C: Combination All Patients #> (N=134) (N=134) (N=132) (N=400) #> ———————————————————————————————————————————————————————————————————————————————— @@ -371,16 +341,16 @@ Demographic Table -lyt <- basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients(): -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred. a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -236,17 +206,17 @@ ArgumentsValue count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout. s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag. -a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE. a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,16 +191,16 @@ ArgumentsValue count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout. s_count_values() returns output of s_summary() for specified values of a non-numeric variable. -a_count_values() returns the corresponding list with formatted rtables::CellValue(). +a_count_values() returns the corresponding list with formatted rtables::CellValue(). Functions count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_values(): S3 generic function to count values. s_count_values(character): Method for character class. s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -212,7 +182,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -238,10 +208,10 @@ ArgumentsValue summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout. s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s). -a_coxreg() returns formatted rtables::CellValue(). +a_coxreg() returns formatted rtables::CellValue(). Details @@ -257,11 +227,11 @@ Functionsrtables::analyze_colvars() and rtables::summarize_row_groups(). +is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups(). s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list. -a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg(). +a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg(). See also diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html index 6124a8f4c1..b15b4c925c 100644 --- a/main/reference/cox_regression_inter.html +++ b/main/reference/cox_regression_inter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -210,7 +180,7 @@ Note Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table(). +variables before passing the dataset to rtables::build_table(). diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html index 976e7c3dcc..c3759b2a0d 100644 --- a/main/reference/cut_quantile_bins.html +++ b/main/reference/cut_quantile_bins.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html index 5b9e065ed8..9cba629d12 100644 --- a/main/reference/d_count_abnormal_by_baseline.html +++ b/main/reference/d_count_abnormal_by_baseline.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html index 8f5e09613a..ed30a89198 100644 --- a/main/reference/d_count_cumulative.html +++ b/main/reference/d_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_missed_doses.html b/main/reference/d_count_missed_doses.html index 3983179580..88d7cb115f 100644 --- a/main/reference/d_count_missed_doses.html +++ b/main/reference/d_count_missed_doses.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_onco_rsp_label.html b/main/reference/d_onco_rsp_label.html index 6ad11d55b2..36eb740b33 100644 --- a/main/reference/d_onco_rsp_label.html +++ b/main/reference/d_onco_rsp_label.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html index 50dd3d760f..911054124b 100644 --- a/main/reference/d_pkparam.html +++ b/main/reference/d_pkparam.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html index 45eecb0d3c..8a8f84d672 100644 --- a/main/reference/d_proportion.html +++ b/main/reference/d_proportion.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html index 513d2d47ae..fc4b954e11 100644 --- a/main/reference/d_proportion_diff.html +++ b/main/reference/d_proportion_diff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html index 953122b65f..cdcbc5e18c 100644 --- a/main/reference/d_rsp_subgroups_colvars.html +++ b/main/reference/d_rsp_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html index 8974168b2e..ad93487971 100644 --- a/main/reference/d_survival_subgroups_colvars.html +++ b/main/reference/d_survival_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html index f32963f964..3c31b90c80 100644 --- a/main/reference/d_test_proportion_diff.html +++ b/main/reference/d_test_proportion_diff.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/day2month.html b/main/reference/day2month.html index 4cdf9eb8d3..00a2059911 100644 --- a/main/reference/day2month.html +++ b/main/reference/day2month.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob.html b/main/reference/decorate_grob.html index 05bab36561..e0ee545cc9 100644 --- a/main/reference/decorate_grob.html +++ b/main/reference/decorate_grob.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_factory.html b/main/reference/decorate_grob_factory.html index bf15634600..c128f2d675 100644 --- a/main/reference/decorate_grob_factory.html +++ b/main/reference/decorate_grob_factory.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_set.html b/main/reference/decorate_grob_set.html index 5193dd3c1b..0de85e9334 100644 --- a/main/reference/decorate_grob_set.html +++ b/main/reference/decorate_grob_set.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_na_str.html b/main/reference/default_na_str.html index 53dc58920e..406deecf68 100644 --- a/main/reference/default_na_str.html +++ b/main/reference/default_na_str.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_stats_formats_labels.html b/main/reference/default_stats_formats_labels.html index 9969f93d9f..f98b60bff5 100644 --- a/main/reference/default_stats_formats_labels.html +++ b/main/reference/default_stats_formats_labels.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,7 +193,7 @@ Note These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings. Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables"). +return a string. This is well documented in vignette("custom_appearance", package = "rtables"). See also diff --git a/main/reference/desctools_binom.html b/main/reference/desctools_binom.html index 4b01634fe0..6be9e52e5c 100644 --- a/main/reference/desctools_binom.html +++ b/main/reference/desctools_binom.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df2gg.html b/main/reference/df2gg.html index d2a0c06ec1..3459218bc2 100644 --- a/main/reference/df2gg.html +++ b/main/reference/df2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df_explicit_na.html b/main/reference/df_explicit_na.html index 7239b2d0e5..3e4a639463 100644 --- a/main/reference/df_explicit_na.html +++ b/main/reference/df_explicit_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/dot-is_equal_float.html b/main/reference/dot-is_equal_float.html index 0e13761934..6c1e53f091 100644 --- a/main/reference/dot-is_equal_float.html +++ b/main/reference/dot-is_equal_float.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/draw_grob.html b/main/reference/draw_grob.html index 451b8e7fd6..f6cdcb0a01 100644 --- a/main/reference/draw_grob.html +++ b/main/reference/draw_grob.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/empty_vector_if_na.html b/main/reference/empty_vector_if_na.html index 7607d61187..452a306105 100644 --- a/main/reference/empty_vector_if_na.html +++ b/main/reference/empty_vector_if_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_coef.html b/main/reference/estimate_coef.html index 2bc5525f87..ce5485013a 100644 --- a/main/reference/estimate_coef.html +++ b/main/reference/estimate_coef.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_multinomial_rsp.html b/main/reference/estimate_multinomial_rsp.html index c917481057..4960eb095e 100644 --- a/main/reference/estimate_multinomial_rsp.html +++ b/main/reference/estimate_multinomial_rsp.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -177,17 +147,17 @@ ArgumentsValue estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout. s_length_proportion() returns statistics from s_proportion(). -a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::analyze() and +rtables::summarize_row_groups(). s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion(). a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -244,17 +214,17 @@ ArgumentsValue estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout. s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable. -a_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion(): Statistics function estimating a proportion along with its confidence interval. a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html index a19da4827a..75158ed039 100644 --- a/main/reference/explicit_na.html +++ b/main/reference/explicit_na.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html index 890e00c7b2..7f4ea02325 100644 --- a/main/reference/extract_by_name.html +++ b/main/reference/extract_by_name.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_biomarkers.html b/main/reference/extract_rsp_biomarkers.html index 47f4183173..22d0b045c8 100644 --- a/main/reference/extract_rsp_biomarkers.html +++ b/main/reference/extract_rsp_biomarkers.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_subgroups.html b/main/reference/extract_rsp_subgroups.html index 10e7fd68dd..bca8059217 100644 --- a/main/reference/extract_rsp_subgroups.html +++ b/main/reference/extract_rsp_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_biomarkers.html b/main/reference/extract_survival_biomarkers.html index 4756961aa7..353e5cea60 100644 --- a/main/reference/extract_survival_biomarkers.html +++ b/main/reference/extract_survival_biomarkers.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_subgroups.html b/main/reference/extract_survival_subgroups.html index 825bc1eedd..fc3a8da8f2 100644 --- a/main/reference/extract_survival_subgroups.html +++ b/main/reference/extract_survival_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extreme_format.html b/main/reference/extreme_format.html index cfb5075e63..c363e0dcb0 100644 --- a/main/reference/extreme_format.html +++ b/main/reference/extreme_format.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html index a2fa7874b0..e59e2a03ae 100644 --- a/main/reference/f_conf_level.html +++ b/main/reference/f_conf_level.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html index 1c4fea37df..5d7ab493ce 100644 --- a/main/reference/f_pval.html +++ b/main/reference/f_pval.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html index 83ab24eb82..f332240368 100644 --- a/main/reference/fct_collapse_only.html +++ b/main/reference/fct_collapse_only.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html index d7fb3b6c64..4cac1ccba5 100644 --- a/main/reference/fct_discard.html +++ b/main/reference/fct_discard.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_explicit_na_if.html b/main/reference/fct_explicit_na_if.html index 3bcaf94176..6848982dde 100644 --- a/main/reference/fct_explicit_na_if.html +++ b/main/reference/fct_explicit_na_if.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_coxreg.html b/main/reference/fit_coxreg.html index 14c5d95f6b..5c2bfc26a0 100644 --- a/main/reference/fit_coxreg.html +++ b/main/reference/fit_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html index 0f364c07c5..4e6119b249 100644 --- a/main/reference/fit_logistic.html +++ b/main/reference/fit_logistic.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html index f5d42bfe7c..5f64d2f786 100644 --- a/main/reference/fit_rsp_step.html +++ b/main/reference/fit_rsp_step.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html index b7da54c090..ca68ad9a04 100644 --- a/main/reference/fit_survival_step.html +++ b/main/reference/fit_survival_step.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html index 2e578ce576..964ab27f65 100644 --- a/main/reference/forest_viewport.html +++ b/main/reference/forest_viewport.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html index 66cff1543b..f5b5ca5633 100644 --- a/main/reference/format_auto.html +++ b/main/reference/format_auto.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -106,7 +76,7 @@ Argumentsdt_var (numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered. diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html index 7c89de6365..4559bb997c 100644 --- a/main/reference/format_count_fraction.html +++ b/main/reference/format_count_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html index fb67db8371..ae6e985489 100644 --- a/main/reference/format_count_fraction_fixed_dp.html +++ b/main/reference/format_count_fraction_fixed_dp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html index 9c0e167253..ce2f2e82e2 100644 --- a/main/reference/format_count_fraction_lt10.html +++ b/main/reference/format_count_fraction_lt10.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html index c12a059e98..6fdb3dd26f 100644 --- a/main/reference/format_extreme_values.html +++ b/main/reference/format_extreme_values.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html index dea1549bfe..c60b369a7d 100644 --- a/main/reference/format_extreme_values_ci.html +++ b/main/reference/format_extreme_values_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html index 8d4274cffb..2c2145230d 100644 --- a/main/reference/format_fraction.html +++ b/main/reference/format_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html index 297df429d5..3c5ba714ad 100644 --- a/main/reference/format_fraction_fixed_dp.html +++ b/main/reference/format_fraction_fixed_dp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_threshold.html b/main/reference/format_fraction_threshold.html index fc7c8ddea7..def9b6a40e 100644 --- a/main/reference/format_fraction_threshold.html +++ b/main/reference/format_fraction_threshold.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_sigfig.html b/main/reference/format_sigfig.html index 02a2b8591e..692c3a6abc 100644 --- a/main/reference/format_sigfig.html +++ b/main/reference/format_sigfig.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_xx.html b/main/reference/format_xx.html index 0ed5c9a3d8..18bbe09129 100644 --- a/main/reference/format_xx.html +++ b/main/reference/format_xx.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html index d325263a97..13af4631b1 100644 --- a/main/reference/formatting_functions.html +++ b/main/reference/formatting_functions.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_forest.html b/main/reference/g_forest.html index 3c11db2339..90308fb9b9 100644 --- a/main/reference/g_forest.html +++ b/main/reference/g_forest.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,7 +191,7 @@ Value Details - Given a rtables::rtable() object with at least one column with a single value and one column with 2 + Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side. diff --git a/main/reference/g_ipp.html b/main/reference/g_ipp.html index a0fe570f3e..187580ab44 100644 --- a/main/reference/g_ipp.html +++ b/main/reference/g_ipp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_km.html b/main/reference/g_km.html index cdfc1967d3..f352196584 100644 --- a/main/reference/g_km.html +++ b/main/reference/g_km.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html index 19a73c87f9..f690b652d5 100644 --- a/main/reference/g_lineplot.html +++ b/main/reference/g_lineplot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_step.html b/main/reference/g_step.html index c610853a23..eb4f82b05a 100644 --- a/main/reference/g_step.html +++ b/main/reference/g_step.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html index cfd51f294f..de2d4f49d4 100644 --- a/main/reference/g_waterfall.html +++ b/main/reference/g_waterfall.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_covariates.html b/main/reference/get_covariates.html index f17bf37bfe..ed76800bad 100644 --- a/main/reference/get_covariates.html +++ b/main/reference/get_covariates.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html index 6214ffa6c9..6215eb9b9d 100644 --- a/main/reference/get_smooths.html +++ b/main/reference/get_smooths.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html index 6e3a623ff0..b5b046e9bb 100644 --- a/main/reference/groups_list_to_df.html +++ b/main/reference/groups_list_to_df.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -76,7 +46,7 @@ - This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). + This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). diff --git a/main/reference/h_adlb_abnormal_by_worst_grade.html b/main/reference/h_adlb_abnormal_by_worst_grade.html index 924b3126fd..124a0f60f6 100644 --- a/main/reference/h_adlb_abnormal_by_worst_grade.html +++ b/main/reference/h_adlb_abnormal_by_worst_grade.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html index c86690c603..a1da8de34a 100644 --- a/main/reference/h_adlb_worsen.html +++ b/main/reference/h_adlb_worsen.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html index 5fc0bf7381..b1b901e4e0 100644 --- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html +++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ancova.html b/main/reference/h_ancova.html index 27bf03975f..59f11617c8 100644 --- a/main/reference/h_ancova.html +++ b/main/reference/h_ancova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_append_grade_groups.html b/main/reference/h_append_grade_groups.html index 2776a060c9..40ba81d7bd 100644 --- a/main/reference/h_append_grade_groups.html +++ b/main/reference/h_append_grade_groups.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html index 06b97cf510..cbaecea676 100644 --- a/main/reference/h_col_indices.html +++ b/main/reference/h_col_indices.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html index 36c0c9a1e1..5d58afe576 100644 --- a/main/reference/h_count_cumulative.html +++ b/main/reference/h_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html index 4be3ea8680..2300696d50 100644 --- a/main/reference/h_cox_regression.html +++ b/main/reference/h_cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html index 7ac8b43b49..54b0d7080d 100644 --- a/main/reference/h_data_plot.html +++ b/main/reference/h_data_plot.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html index 4cf323ad4e..96502e49d1 100644 --- a/main/reference/h_decompose_gg.html +++ b/main/reference/h_decompose_gg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html index 1033ff5a0c..11071684bb 100644 --- a/main/reference/h_format_row.html +++ b/main/reference/h_format_row.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -95,7 +65,7 @@ Argumentsformat (named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter. diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html index ba609f3d69..d4c8203cba 100644 --- a/main/reference/h_g_ipp.html +++ b/main/reference/h_g_ipp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ggkm.html b/main/reference/h_ggkm.html index 512f6a9180..0aac0f9127 100644 --- a/main/reference/h_ggkm.html +++ b/main/reference/h_ggkm.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_glm_count.html b/main/reference/h_glm_count.html index 036db54acf..74a4e7900b 100644 --- a/main/reference/h_glm_count.html +++ b/main/reference/h_glm_count.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html index 7dcead7d01..132d15c26a 100644 --- a/main/reference/h_grob_coxph.html +++ b/main/reference/h_grob_coxph.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html index 82ea2d549f..60bd8ff8d5 100644 --- a/main/reference/h_grob_median_surv.html +++ b/main/reference/h_grob_median_surv.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html index 3af28455fb..7879a81306 100644 --- a/main/reference/h_grob_tbl_at_risk.html +++ b/main/reference/h_grob_tbl_at_risk.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html index 48a9227ad3..dd46627d90 100644 --- a/main/reference/h_grob_y_annot.html +++ b/main/reference/h_grob_y_annot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html index 77eb4bd003..e85621d716 100644 --- a/main/reference/h_incidence_rate.html +++ b/main/reference/h_incidence_rate.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html index 9e3fdc86ed..67d651af4a 100644 --- a/main/reference/h_km_layout.html +++ b/main/reference/h_km_layout.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html index fe6cbfe730..8dd16febea 100644 --- a/main/reference/h_logistic_regression.html +++ b/main/reference/h_logistic_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html index 10495898fc..59f19b1f76 100644 --- a/main/reference/h_map_for_count_abnormal.html +++ b/main/reference/h_map_for_count_abnormal.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html index e1e17eedc3..b93837e390 100644 --- a/main/reference/h_odds_ratio.html +++ b/main/reference/h_odds_ratio.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html index 982b2989b6..318bbf2ecc 100644 --- a/main/reference/h_pkparam_sort.html +++ b/main/reference/h_pkparam_sort.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ppmeans.html b/main/reference/h_ppmeans.html index 0ce7208de8..57fe7b7773 100644 --- a/main/reference/h_ppmeans.html +++ b/main/reference/h_ppmeans.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html index 82c09a19d9..df5cd7a011 100644 --- a/main/reference/h_prop_diff.html +++ b/main/reference/h_prop_diff.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html index 879561bbee..7ebaf8c228 100644 --- a/main/reference/h_prop_diff_test.html +++ b/main/reference/h_prop_diff_test.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_proportions.html b/main/reference/h_proportions.html index e2454b4cec..cae1aa23c0 100644 --- a/main/reference/h_proportions.html +++ b/main/reference/h_proportions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_biomarkers_subgroups.html b/main/reference/h_response_biomarkers_subgroups.html index 6253742dc3..d40c44e05a 100644 --- a/main/reference/h_response_biomarkers_subgroups.html +++ b/main/reference/h_response_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_subgroups.html b/main/reference/h_response_subgroups.html index f8553643c3..3277639d7f 100644 --- a/main/reference/h_response_subgroups.html +++ b/main/reference/h_response_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_by_subgroups.html b/main/reference/h_split_by_subgroups.html index 30b5234a3f..53bbd7df94 100644 --- a/main/reference/h_split_by_subgroups.html +++ b/main/reference/h_split_by_subgroups.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html index 1c85bd0acc..f531b50248 100644 --- a/main/reference/h_split_param.html +++ b/main/reference/h_split_param.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_stack_by_baskets.html b/main/reference/h_stack_by_baskets.html index 856a6bfda2..6f466ed562 100644 --- a/main/reference/h_stack_by_baskets.html +++ b/main/reference/h_stack_by_baskets.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_step.html b/main/reference/h_step.html index 22264810df..aed7400437 100644 --- a/main/reference/h_step.html +++ b/main/reference/h_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html index e2ef402488..24d8bdff48 100644 --- a/main/reference/h_survival_biomarkers_subgroups.html +++ b/main/reference/h_survival_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html index f45ef9d1ed..27a3f5c549 100644 --- a/main/reference/h_survival_duration_subgroups.html +++ b/main/reference/h_survival_duration_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html index ca44867180..75abc5d17b 100644 --- a/main/reference/h_tab_one_biomarker.html +++ b/main/reference/h_tab_one_biomarker.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups(). +This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html index fb85cb68ff..d06f1ec802 100644 --- a/main/reference/h_tbl_coxph_pairwise.html +++ b/main/reference/h_tbl_coxph_pairwise.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html index 824c9b6ba7..6c36b258c8 100644 --- a/main/reference/h_tbl_median_surv.html +++ b/main/reference/h_tbl_median_surv.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html index bd3da774c9..75cee2d4f8 100644 --- a/main/reference/h_worsen_counter.html +++ b/main/reference/h_worsen_counter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html index 3b00b0578e..cdca04f44b 100644 --- a/main/reference/h_xticks.html +++ b/main/reference/h_xticks.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html index 4af234fef7..9793443aea 100644 --- a/main/reference/imputation_rule.html +++ b/main/reference/imputation_rule.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html index 54c857ff9c..c6a5c87c16 100644 --- a/main/reference/incidence_rate.html +++ b/main/reference/incidence_rate.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -239,7 +209,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -251,7 +221,7 @@ ArgumentsValue estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout. s_incidence_rate() returns the following statistics:person_years: Total person-years at risk. n_events: Total number of events observed. @@ -260,13 +230,13 @@ Valuen_unique: Total number of patients with at least one event observed. n_rate: Total number of events observed & estimated incidence rate. -a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). +a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval. a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate(). diff --git a/main/reference/index.html b/main/reference/index.html index 2a1665f99f..09b3124477 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -196,7 +166,7 @@ Analysis FunctionsAnalyze functions with their corresponding statistics functions and formatted analysis functions.Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table. Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves. -Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). +Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html index 3f1016c509..dd93cf81ce 100644 --- a/main/reference/labels_or_names.html +++ b/main/reference/labels_or_names.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/labels_use_control.html b/main/reference/labels_use_control.html index 5c301b4228..d4006f99f7 100644 --- a/main/reference/labels_use_control.html +++ b/main/reference/labels_use_control.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html index 7744090684..8accd2e89b 100644 --- a/main/reference/logistic_regression_cols.html +++ b/main/reference/logistic_regression_cols.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar(). +regression results. This function is a wrapper for rtables::split_cols_by_multivar(). diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html index 9807a24c22..45cb84b416 100644 --- a/main/reference/logistic_summary_by_flag.html +++ b/main/reference/logistic_summary_by_flag.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). +logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/make_names.html b/main/reference/make_names.html index d14c4af69d..ef3f426440 100644 --- a/main/reference/make_names.html +++ b/main/reference/make_names.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/month2day.html b/main/reference/month2day.html index 87004ac14c..bb59b07ac8 100644 --- a/main/reference/month2day.html +++ b/main/reference/month2day.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/muffled_car_anova.html b/main/reference/muffled_car_anova.html index 3d902766c3..e4c73d2ed5 100644 --- a/main/reference/muffled_car_anova.html +++ b/main/reference/muffled_car_anova.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/n_available.html b/main/reference/n_available.html index af70552f25..d8856b8358 100644 --- a/main/reference/n_available.html +++ b/main/reference/n_available.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html index 3bcb51179d..58a1801d4a 100644 --- a/main/reference/odds_ratio.html +++ b/main/reference/odds_ratio.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -228,17 +198,17 @@ ArgumentsValue estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout. s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot. -a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). +a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required. diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html index 51e9d86731..bd76dd200f 100644 --- a/main/reference/prop_diff.html +++ b/main/reference/prop_diff.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -237,16 +207,16 @@ ArgumentsValue estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout. s_proportion_diff() returns a named list of elements diff and diff_ci. -a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion. a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff(). diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html index 17bf534958..d4bfbab57f 100644 --- a/main/reference/prop_diff_test.html +++ b/main/reference/prop_diff_test.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,17 +187,17 @@ ArgumentsValue test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout. s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same. -a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_test_proportion_diff(): Statistics function which tests the difference between two proportions. a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff(). diff --git a/main/reference/prune_occurrences.html b/main/reference/prune_occurrences.html index 74e764167c..75f049f5b1 100644 --- a/main/reference/prune_occurrences.html +++ b/main/reference/prune_occurrences.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -137,7 +107,7 @@ Arguments Value -keep_rows() returns a pruning function that can be used with rtables::prune_table() +keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table. keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table. @@ -180,7 +150,7 @@ FunctionsNote Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface. +should be pruned, to conform with the rtables::prune_table() interface. diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html index ccf69253f9..8bcad27b16 100644 --- a/main/reference/range_noinf.html +++ b/main/reference/range_noinf.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html index 3369960273..c35e1c50c9 100644 --- a/main/reference/reapply_varlabels.html +++ b/main/reference/reapply_varlabels.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html index 53816fe9b9..4c72961f2a 100644 --- a/main/reference/response_biomarkers_subgroups.html +++ b/main/reference/response_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html index fb6d448883..a3f14ce1b6 100644 --- a/main/reference/response_subgroups.html +++ b/main/reference/response_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -171,7 +141,7 @@ Arguments Value An rtables table summarizing binary response by subgroup. -a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -182,8 +152,8 @@ DetailsFunctions tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups(). diff --git a/main/reference/rtable2gg.html b/main/reference/rtable2gg.html index 6068cb1bb8..cdf46a633f 100644 --- a/main/reference/rtable2gg.html +++ b/main/reference/rtable2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,7 +51,7 @@ -Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored. diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html index 0f6db1ef4c..67c8394ca2 100644 --- a/main/reference/rtables_access.html +++ b/main/reference/rtables_access.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html index 2198ad15a7..445c4fd861 100644 --- a/main/reference/s_cox_multivariate.html +++ b/main/reference/s_cox_multivariate.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html index fa683b677a..8c4b03b9ab 100644 --- a/main/reference/sas_na.html +++ b/main/reference/sas_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html index f5b86fe70a..3815c64608 100644 --- a/main/reference/score_occurrences.html +++ b/main/reference/score_occurrences.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Functionsrtables::cont_n_onecol() and rtables::cont_n_allcols(). +specified columns. Note that this is extending rtables::cont_n_onecol() and rtables::cont_n_allcols(). See also diff --git a/main/reference/split_cols_by_groups.html b/main/reference/split_cols_by_groups.html index 3c97f5cfee..f7bfcad622 100644 --- a/main/reference/split_cols_by_groups.html +++ b/main/reference/split_cols_by_groups.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -107,7 +77,7 @@ Arguments... -additional arguments to rtables::split_cols_by() in order. For instance, to +additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all). @@ -119,7 +89,7 @@ Value See also - rtables::split_cols_by() + rtables::split_cols_by() diff --git a/main/reference/split_text_grob.html b/main/reference/split_text_grob.html index 8671c0d8d3..062cbc39d2 100644 --- a/main/reference/split_text_grob.html +++ b/main/reference/split_text_grob.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html index fb97a58911..6f44c5774b 100644 --- a/main/reference/stack_grobs.html +++ b/main/reference/stack_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html index ffa8368b70..c31e318dd4 100644 --- a/main/reference/stat_mean_ci.html +++ b/main/reference/stat_mean_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html index ef90542a4e..6663e7ee57 100644 --- a/main/reference/stat_mean_pval.html +++ b/main/reference/stat_mean_pval.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_median_ci.html b/main/reference/stat_median_ci.html index ea5d28e072..f43024d65c 100644 --- a/main/reference/stat_median_ci.html +++ b/main/reference/stat_median_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_propdiff_ci.html b/main/reference/stat_propdiff_ci.html index b7119ac644..5db044c950 100644 --- a/main/reference/stat_propdiff_ci.html +++ b/main/reference/stat_propdiff_ci.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -141,7 +111,7 @@ Value See also - Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() + Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout. diff --git a/main/reference/strata_normal_quantile.html b/main/reference/strata_normal_quantile.html index afc45b01e1..82d6de6e3f 100644 --- a/main/reference/strata_normal_quantile.html +++ b/main/reference/strata_normal_quantile.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/study_arm.html b/main/reference/study_arm.html index 4a60dcedf5..67999fc7aa 100644 --- a/main/reference/study_arm.html +++ b/main/reference/study_arm.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/summarize_ancova.html b/main/reference/summarize_ancova.html index 1bf16f8b79..1eba34c24c 100644 --- a/main/reference/summarize_ancova.html +++ b/main/reference/summarize_ancova.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -254,7 +224,7 @@ ArgumentsValue summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout. s_ancova() returns a named list of 5 statistics:n: Count of complete sample size for the group. lsmean: Estimated marginal means in the group. @@ -264,13 +234,13 @@ Value pval: p-value (not adjusted for multiple comparisons). -a_ancova() returns the corresponding list with formatted rtables::CellValue(). +a_ancova() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_ancova(): Statistics function that produces a named list of results of the investigated linear model. a_ancova(): Formatted analysis function which is used as afun in summarize_ancova(). diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html index 70d34fcade..83fc422b89 100644 --- a/main/reference/summarize_change.html +++ b/main/reference/summarize_change.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -192,16 +162,16 @@ ArgumentsValue summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout. s_change_from_baseline() returns the same values returned by s_summary.numeric(). -a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits. a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change(). diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html index 5f7d10a3ef..2caae0ef34 100644 --- a/main/reference/summarize_colvars.html +++ b/main/reference/summarize_colvars.html @@ -2,13 +2,13 @@ Summarize variables in columns — summarize_colvars • tern @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -91,8 +61,8 @@ The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars(). -The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars(). +The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function. @@ -147,13 +117,13 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. See also - rtables::split_cols_by_multivar() and analyze_colvars_functions. + rtables::split_cols_by_multivar() and analyze_colvars_functions. diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html index 751617921c..34ebaf2237 100644 --- a/main/reference/summarize_functions.html +++ b/main/reference/summarize_functions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -78,7 +48,7 @@ - These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions + These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout: @@ -87,7 +57,7 @@ Details add_rowcounts() -estimate_multinomial_response() (with rtables::analyze()) +estimate_multinomial_response() (with rtables::analyze()) h_tab_one_biomarker() (probably to deprecate) logistic_summary_by_flag() summarize_num_patients() @@ -95,15 +65,15 @@ Detailssummarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() -Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table.
-lyt <- basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients(): -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred. a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -236,17 +206,17 @@ ArgumentsValue count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout. s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag. -a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE. a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,16 +191,16 @@ ArgumentsValue count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout. s_count_values() returns output of s_summary() for specified values of a non-numeric variable. -a_count_values() returns the corresponding list with formatted rtables::CellValue(). +a_count_values() returns the corresponding list with formatted rtables::CellValue(). Functions count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_values(): S3 generic function to count values. s_count_values(character): Method for character class. s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -212,7 +182,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -238,10 +208,10 @@ ArgumentsValue summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout. s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s). -a_coxreg() returns formatted rtables::CellValue(). +a_coxreg() returns formatted rtables::CellValue(). Details @@ -257,11 +227,11 @@ Functionsrtables::analyze_colvars() and rtables::summarize_row_groups(). +is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups(). s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list. -a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg(). +a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg(). See also diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html index 6124a8f4c1..b15b4c925c 100644 --- a/main/reference/cox_regression_inter.html +++ b/main/reference/cox_regression_inter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -210,7 +180,7 @@ Note Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table(). +variables before passing the dataset to rtables::build_table(). diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html index 976e7c3dcc..c3759b2a0d 100644 --- a/main/reference/cut_quantile_bins.html +++ b/main/reference/cut_quantile_bins.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html index 5b9e065ed8..9cba629d12 100644 --- a/main/reference/d_count_abnormal_by_baseline.html +++ b/main/reference/d_count_abnormal_by_baseline.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html index 8f5e09613a..ed30a89198 100644 --- a/main/reference/d_count_cumulative.html +++ b/main/reference/d_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_missed_doses.html b/main/reference/d_count_missed_doses.html index 3983179580..88d7cb115f 100644 --- a/main/reference/d_count_missed_doses.html +++ b/main/reference/d_count_missed_doses.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_onco_rsp_label.html b/main/reference/d_onco_rsp_label.html index 6ad11d55b2..36eb740b33 100644 --- a/main/reference/d_onco_rsp_label.html +++ b/main/reference/d_onco_rsp_label.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html index 50dd3d760f..911054124b 100644 --- a/main/reference/d_pkparam.html +++ b/main/reference/d_pkparam.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html index 45eecb0d3c..8a8f84d672 100644 --- a/main/reference/d_proportion.html +++ b/main/reference/d_proportion.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html index 513d2d47ae..fc4b954e11 100644 --- a/main/reference/d_proportion_diff.html +++ b/main/reference/d_proportion_diff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html index 953122b65f..cdcbc5e18c 100644 --- a/main/reference/d_rsp_subgroups_colvars.html +++ b/main/reference/d_rsp_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html index 8974168b2e..ad93487971 100644 --- a/main/reference/d_survival_subgroups_colvars.html +++ b/main/reference/d_survival_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html index f32963f964..3c31b90c80 100644 --- a/main/reference/d_test_proportion_diff.html +++ b/main/reference/d_test_proportion_diff.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/day2month.html b/main/reference/day2month.html index 4cdf9eb8d3..00a2059911 100644 --- a/main/reference/day2month.html +++ b/main/reference/day2month.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob.html b/main/reference/decorate_grob.html index 05bab36561..e0ee545cc9 100644 --- a/main/reference/decorate_grob.html +++ b/main/reference/decorate_grob.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_factory.html b/main/reference/decorate_grob_factory.html index bf15634600..c128f2d675 100644 --- a/main/reference/decorate_grob_factory.html +++ b/main/reference/decorate_grob_factory.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_set.html b/main/reference/decorate_grob_set.html index 5193dd3c1b..0de85e9334 100644 --- a/main/reference/decorate_grob_set.html +++ b/main/reference/decorate_grob_set.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_na_str.html b/main/reference/default_na_str.html index 53dc58920e..406deecf68 100644 --- a/main/reference/default_na_str.html +++ b/main/reference/default_na_str.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_stats_formats_labels.html b/main/reference/default_stats_formats_labels.html index 9969f93d9f..f98b60bff5 100644 --- a/main/reference/default_stats_formats_labels.html +++ b/main/reference/default_stats_formats_labels.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,7 +193,7 @@ Note These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings. Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables"). +return a string. This is well documented in vignette("custom_appearance", package = "rtables"). See also diff --git a/main/reference/desctools_binom.html b/main/reference/desctools_binom.html index 4b01634fe0..6be9e52e5c 100644 --- a/main/reference/desctools_binom.html +++ b/main/reference/desctools_binom.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df2gg.html b/main/reference/df2gg.html index d2a0c06ec1..3459218bc2 100644 --- a/main/reference/df2gg.html +++ b/main/reference/df2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df_explicit_na.html b/main/reference/df_explicit_na.html index 7239b2d0e5..3e4a639463 100644 --- a/main/reference/df_explicit_na.html +++ b/main/reference/df_explicit_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/dot-is_equal_float.html b/main/reference/dot-is_equal_float.html index 0e13761934..6c1e53f091 100644 --- a/main/reference/dot-is_equal_float.html +++ b/main/reference/dot-is_equal_float.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/draw_grob.html b/main/reference/draw_grob.html index 451b8e7fd6..f6cdcb0a01 100644 --- a/main/reference/draw_grob.html +++ b/main/reference/draw_grob.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/empty_vector_if_na.html b/main/reference/empty_vector_if_na.html index 7607d61187..452a306105 100644 --- a/main/reference/empty_vector_if_na.html +++ b/main/reference/empty_vector_if_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_coef.html b/main/reference/estimate_coef.html index 2bc5525f87..ce5485013a 100644 --- a/main/reference/estimate_coef.html +++ b/main/reference/estimate_coef.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_multinomial_rsp.html b/main/reference/estimate_multinomial_rsp.html index c917481057..4960eb095e 100644 --- a/main/reference/estimate_multinomial_rsp.html +++ b/main/reference/estimate_multinomial_rsp.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -177,17 +147,17 @@ ArgumentsValue estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout. s_length_proportion() returns statistics from s_proportion(). -a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::analyze() and +rtables::summarize_row_groups(). s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion(). a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -244,17 +214,17 @@ ArgumentsValue estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout. s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable. -a_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion(): Statistics function estimating a proportion along with its confidence interval. a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html index a19da4827a..75158ed039 100644 --- a/main/reference/explicit_na.html +++ b/main/reference/explicit_na.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html index 890e00c7b2..7f4ea02325 100644 --- a/main/reference/extract_by_name.html +++ b/main/reference/extract_by_name.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_biomarkers.html b/main/reference/extract_rsp_biomarkers.html index 47f4183173..22d0b045c8 100644 --- a/main/reference/extract_rsp_biomarkers.html +++ b/main/reference/extract_rsp_biomarkers.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_subgroups.html b/main/reference/extract_rsp_subgroups.html index 10e7fd68dd..bca8059217 100644 --- a/main/reference/extract_rsp_subgroups.html +++ b/main/reference/extract_rsp_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_biomarkers.html b/main/reference/extract_survival_biomarkers.html index 4756961aa7..353e5cea60 100644 --- a/main/reference/extract_survival_biomarkers.html +++ b/main/reference/extract_survival_biomarkers.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_subgroups.html b/main/reference/extract_survival_subgroups.html index 825bc1eedd..fc3a8da8f2 100644 --- a/main/reference/extract_survival_subgroups.html +++ b/main/reference/extract_survival_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extreme_format.html b/main/reference/extreme_format.html index cfb5075e63..c363e0dcb0 100644 --- a/main/reference/extreme_format.html +++ b/main/reference/extreme_format.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html index a2fa7874b0..e59e2a03ae 100644 --- a/main/reference/f_conf_level.html +++ b/main/reference/f_conf_level.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html index 1c4fea37df..5d7ab493ce 100644 --- a/main/reference/f_pval.html +++ b/main/reference/f_pval.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html index 83ab24eb82..f332240368 100644 --- a/main/reference/fct_collapse_only.html +++ b/main/reference/fct_collapse_only.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html index d7fb3b6c64..4cac1ccba5 100644 --- a/main/reference/fct_discard.html +++ b/main/reference/fct_discard.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_explicit_na_if.html b/main/reference/fct_explicit_na_if.html index 3bcaf94176..6848982dde 100644 --- a/main/reference/fct_explicit_na_if.html +++ b/main/reference/fct_explicit_na_if.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_coxreg.html b/main/reference/fit_coxreg.html index 14c5d95f6b..5c2bfc26a0 100644 --- a/main/reference/fit_coxreg.html +++ b/main/reference/fit_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html index 0f364c07c5..4e6119b249 100644 --- a/main/reference/fit_logistic.html +++ b/main/reference/fit_logistic.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html index f5d42bfe7c..5f64d2f786 100644 --- a/main/reference/fit_rsp_step.html +++ b/main/reference/fit_rsp_step.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html index b7da54c090..ca68ad9a04 100644 --- a/main/reference/fit_survival_step.html +++ b/main/reference/fit_survival_step.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html index 2e578ce576..964ab27f65 100644 --- a/main/reference/forest_viewport.html +++ b/main/reference/forest_viewport.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html index 66cff1543b..f5b5ca5633 100644 --- a/main/reference/format_auto.html +++ b/main/reference/format_auto.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -106,7 +76,7 @@ Argumentsdt_var (numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered. diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html index 7c89de6365..4559bb997c 100644 --- a/main/reference/format_count_fraction.html +++ b/main/reference/format_count_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html index fb67db8371..ae6e985489 100644 --- a/main/reference/format_count_fraction_fixed_dp.html +++ b/main/reference/format_count_fraction_fixed_dp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html index 9c0e167253..ce2f2e82e2 100644 --- a/main/reference/format_count_fraction_lt10.html +++ b/main/reference/format_count_fraction_lt10.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html index c12a059e98..6fdb3dd26f 100644 --- a/main/reference/format_extreme_values.html +++ b/main/reference/format_extreme_values.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html index dea1549bfe..c60b369a7d 100644 --- a/main/reference/format_extreme_values_ci.html +++ b/main/reference/format_extreme_values_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html index 8d4274cffb..2c2145230d 100644 --- a/main/reference/format_fraction.html +++ b/main/reference/format_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html index 297df429d5..3c5ba714ad 100644 --- a/main/reference/format_fraction_fixed_dp.html +++ b/main/reference/format_fraction_fixed_dp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_threshold.html b/main/reference/format_fraction_threshold.html index fc7c8ddea7..def9b6a40e 100644 --- a/main/reference/format_fraction_threshold.html +++ b/main/reference/format_fraction_threshold.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_sigfig.html b/main/reference/format_sigfig.html index 02a2b8591e..692c3a6abc 100644 --- a/main/reference/format_sigfig.html +++ b/main/reference/format_sigfig.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_xx.html b/main/reference/format_xx.html index 0ed5c9a3d8..18bbe09129 100644 --- a/main/reference/format_xx.html +++ b/main/reference/format_xx.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html index d325263a97..13af4631b1 100644 --- a/main/reference/formatting_functions.html +++ b/main/reference/formatting_functions.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_forest.html b/main/reference/g_forest.html index 3c11db2339..90308fb9b9 100644 --- a/main/reference/g_forest.html +++ b/main/reference/g_forest.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,7 +191,7 @@ Value Details - Given a rtables::rtable() object with at least one column with a single value and one column with 2 + Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side. diff --git a/main/reference/g_ipp.html b/main/reference/g_ipp.html index a0fe570f3e..187580ab44 100644 --- a/main/reference/g_ipp.html +++ b/main/reference/g_ipp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_km.html b/main/reference/g_km.html index cdfc1967d3..f352196584 100644 --- a/main/reference/g_km.html +++ b/main/reference/g_km.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html index 19a73c87f9..f690b652d5 100644 --- a/main/reference/g_lineplot.html +++ b/main/reference/g_lineplot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_step.html b/main/reference/g_step.html index c610853a23..eb4f82b05a 100644 --- a/main/reference/g_step.html +++ b/main/reference/g_step.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html index cfd51f294f..de2d4f49d4 100644 --- a/main/reference/g_waterfall.html +++ b/main/reference/g_waterfall.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_covariates.html b/main/reference/get_covariates.html index f17bf37bfe..ed76800bad 100644 --- a/main/reference/get_covariates.html +++ b/main/reference/get_covariates.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html index 6214ffa6c9..6215eb9b9d 100644 --- a/main/reference/get_smooths.html +++ b/main/reference/get_smooths.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html index 6e3a623ff0..b5b046e9bb 100644 --- a/main/reference/groups_list_to_df.html +++ b/main/reference/groups_list_to_df.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -76,7 +46,7 @@ - This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). + This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). diff --git a/main/reference/h_adlb_abnormal_by_worst_grade.html b/main/reference/h_adlb_abnormal_by_worst_grade.html index 924b3126fd..124a0f60f6 100644 --- a/main/reference/h_adlb_abnormal_by_worst_grade.html +++ b/main/reference/h_adlb_abnormal_by_worst_grade.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html index c86690c603..a1da8de34a 100644 --- a/main/reference/h_adlb_worsen.html +++ b/main/reference/h_adlb_worsen.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html index 5fc0bf7381..b1b901e4e0 100644 --- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html +++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ancova.html b/main/reference/h_ancova.html index 27bf03975f..59f11617c8 100644 --- a/main/reference/h_ancova.html +++ b/main/reference/h_ancova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_append_grade_groups.html b/main/reference/h_append_grade_groups.html index 2776a060c9..40ba81d7bd 100644 --- a/main/reference/h_append_grade_groups.html +++ b/main/reference/h_append_grade_groups.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html index 06b97cf510..cbaecea676 100644 --- a/main/reference/h_col_indices.html +++ b/main/reference/h_col_indices.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html index 36c0c9a1e1..5d58afe576 100644 --- a/main/reference/h_count_cumulative.html +++ b/main/reference/h_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html index 4be3ea8680..2300696d50 100644 --- a/main/reference/h_cox_regression.html +++ b/main/reference/h_cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html index 7ac8b43b49..54b0d7080d 100644 --- a/main/reference/h_data_plot.html +++ b/main/reference/h_data_plot.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html index 4cf323ad4e..96502e49d1 100644 --- a/main/reference/h_decompose_gg.html +++ b/main/reference/h_decompose_gg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html index 1033ff5a0c..11071684bb 100644 --- a/main/reference/h_format_row.html +++ b/main/reference/h_format_row.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -95,7 +65,7 @@ Argumentsformat (named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter. diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html index ba609f3d69..d4c8203cba 100644 --- a/main/reference/h_g_ipp.html +++ b/main/reference/h_g_ipp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ggkm.html b/main/reference/h_ggkm.html index 512f6a9180..0aac0f9127 100644 --- a/main/reference/h_ggkm.html +++ b/main/reference/h_ggkm.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_glm_count.html b/main/reference/h_glm_count.html index 036db54acf..74a4e7900b 100644 --- a/main/reference/h_glm_count.html +++ b/main/reference/h_glm_count.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html index 7dcead7d01..132d15c26a 100644 --- a/main/reference/h_grob_coxph.html +++ b/main/reference/h_grob_coxph.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html index 82ea2d549f..60bd8ff8d5 100644 --- a/main/reference/h_grob_median_surv.html +++ b/main/reference/h_grob_median_surv.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html index 3af28455fb..7879a81306 100644 --- a/main/reference/h_grob_tbl_at_risk.html +++ b/main/reference/h_grob_tbl_at_risk.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html index 48a9227ad3..dd46627d90 100644 --- a/main/reference/h_grob_y_annot.html +++ b/main/reference/h_grob_y_annot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html index 77eb4bd003..e85621d716 100644 --- a/main/reference/h_incidence_rate.html +++ b/main/reference/h_incidence_rate.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html index 9e3fdc86ed..67d651af4a 100644 --- a/main/reference/h_km_layout.html +++ b/main/reference/h_km_layout.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html index fe6cbfe730..8dd16febea 100644 --- a/main/reference/h_logistic_regression.html +++ b/main/reference/h_logistic_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html index 10495898fc..59f19b1f76 100644 --- a/main/reference/h_map_for_count_abnormal.html +++ b/main/reference/h_map_for_count_abnormal.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html index e1e17eedc3..b93837e390 100644 --- a/main/reference/h_odds_ratio.html +++ b/main/reference/h_odds_ratio.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html index 982b2989b6..318bbf2ecc 100644 --- a/main/reference/h_pkparam_sort.html +++ b/main/reference/h_pkparam_sort.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ppmeans.html b/main/reference/h_ppmeans.html index 0ce7208de8..57fe7b7773 100644 --- a/main/reference/h_ppmeans.html +++ b/main/reference/h_ppmeans.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html index 82c09a19d9..df5cd7a011 100644 --- a/main/reference/h_prop_diff.html +++ b/main/reference/h_prop_diff.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html index 879561bbee..7ebaf8c228 100644 --- a/main/reference/h_prop_diff_test.html +++ b/main/reference/h_prop_diff_test.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_proportions.html b/main/reference/h_proportions.html index e2454b4cec..cae1aa23c0 100644 --- a/main/reference/h_proportions.html +++ b/main/reference/h_proportions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_biomarkers_subgroups.html b/main/reference/h_response_biomarkers_subgroups.html index 6253742dc3..d40c44e05a 100644 --- a/main/reference/h_response_biomarkers_subgroups.html +++ b/main/reference/h_response_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_subgroups.html b/main/reference/h_response_subgroups.html index f8553643c3..3277639d7f 100644 --- a/main/reference/h_response_subgroups.html +++ b/main/reference/h_response_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_by_subgroups.html b/main/reference/h_split_by_subgroups.html index 30b5234a3f..53bbd7df94 100644 --- a/main/reference/h_split_by_subgroups.html +++ b/main/reference/h_split_by_subgroups.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html index 1c85bd0acc..f531b50248 100644 --- a/main/reference/h_split_param.html +++ b/main/reference/h_split_param.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_stack_by_baskets.html b/main/reference/h_stack_by_baskets.html index 856a6bfda2..6f466ed562 100644 --- a/main/reference/h_stack_by_baskets.html +++ b/main/reference/h_stack_by_baskets.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_step.html b/main/reference/h_step.html index 22264810df..aed7400437 100644 --- a/main/reference/h_step.html +++ b/main/reference/h_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html index e2ef402488..24d8bdff48 100644 --- a/main/reference/h_survival_biomarkers_subgroups.html +++ b/main/reference/h_survival_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html index f45ef9d1ed..27a3f5c549 100644 --- a/main/reference/h_survival_duration_subgroups.html +++ b/main/reference/h_survival_duration_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html index ca44867180..75abc5d17b 100644 --- a/main/reference/h_tab_one_biomarker.html +++ b/main/reference/h_tab_one_biomarker.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups(). +This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html index fb85cb68ff..d06f1ec802 100644 --- a/main/reference/h_tbl_coxph_pairwise.html +++ b/main/reference/h_tbl_coxph_pairwise.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html index 824c9b6ba7..6c36b258c8 100644 --- a/main/reference/h_tbl_median_surv.html +++ b/main/reference/h_tbl_median_surv.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html index bd3da774c9..75cee2d4f8 100644 --- a/main/reference/h_worsen_counter.html +++ b/main/reference/h_worsen_counter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html index 3b00b0578e..cdca04f44b 100644 --- a/main/reference/h_xticks.html +++ b/main/reference/h_xticks.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html index 4af234fef7..9793443aea 100644 --- a/main/reference/imputation_rule.html +++ b/main/reference/imputation_rule.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html index 54c857ff9c..c6a5c87c16 100644 --- a/main/reference/incidence_rate.html +++ b/main/reference/incidence_rate.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -239,7 +209,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -251,7 +221,7 @@ ArgumentsValue estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout. s_incidence_rate() returns the following statistics:person_years: Total person-years at risk. n_events: Total number of events observed. @@ -260,13 +230,13 @@ Valuen_unique: Total number of patients with at least one event observed. n_rate: Total number of events observed & estimated incidence rate. -a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). +a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval. a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate(). diff --git a/main/reference/index.html b/main/reference/index.html index 2a1665f99f..09b3124477 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -196,7 +166,7 @@ Analysis FunctionsAnalyze functions with their corresponding statistics functions and formatted analysis functions.Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table. Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves. -Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). +Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html index 3f1016c509..dd93cf81ce 100644 --- a/main/reference/labels_or_names.html +++ b/main/reference/labels_or_names.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/labels_use_control.html b/main/reference/labels_use_control.html index 5c301b4228..d4006f99f7 100644 --- a/main/reference/labels_use_control.html +++ b/main/reference/labels_use_control.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html index 7744090684..8accd2e89b 100644 --- a/main/reference/logistic_regression_cols.html +++ b/main/reference/logistic_regression_cols.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar(). +regression results. This function is a wrapper for rtables::split_cols_by_multivar(). diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html index 9807a24c22..45cb84b416 100644 --- a/main/reference/logistic_summary_by_flag.html +++ b/main/reference/logistic_summary_by_flag.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). +logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/make_names.html b/main/reference/make_names.html index d14c4af69d..ef3f426440 100644 --- a/main/reference/make_names.html +++ b/main/reference/make_names.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/month2day.html b/main/reference/month2day.html index 87004ac14c..bb59b07ac8 100644 --- a/main/reference/month2day.html +++ b/main/reference/month2day.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/muffled_car_anova.html b/main/reference/muffled_car_anova.html index 3d902766c3..e4c73d2ed5 100644 --- a/main/reference/muffled_car_anova.html +++ b/main/reference/muffled_car_anova.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/n_available.html b/main/reference/n_available.html index af70552f25..d8856b8358 100644 --- a/main/reference/n_available.html +++ b/main/reference/n_available.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html index 3bcb51179d..58a1801d4a 100644 --- a/main/reference/odds_ratio.html +++ b/main/reference/odds_ratio.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -228,17 +198,17 @@ ArgumentsValue estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout. s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot. -a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). +a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required. diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html index 51e9d86731..bd76dd200f 100644 --- a/main/reference/prop_diff.html +++ b/main/reference/prop_diff.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -237,16 +207,16 @@ ArgumentsValue estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout. s_proportion_diff() returns a named list of elements diff and diff_ci. -a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion. a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff(). diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html index 17bf534958..d4bfbab57f 100644 --- a/main/reference/prop_diff_test.html +++ b/main/reference/prop_diff_test.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,17 +187,17 @@ ArgumentsValue test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout. s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same. -a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_test_proportion_diff(): Statistics function which tests the difference between two proportions. a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff(). diff --git a/main/reference/prune_occurrences.html b/main/reference/prune_occurrences.html index 74e764167c..75f049f5b1 100644 --- a/main/reference/prune_occurrences.html +++ b/main/reference/prune_occurrences.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -137,7 +107,7 @@ Arguments Value -keep_rows() returns a pruning function that can be used with rtables::prune_table() +keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table. keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table. @@ -180,7 +150,7 @@ FunctionsNote Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface. +should be pruned, to conform with the rtables::prune_table() interface. diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html index ccf69253f9..8bcad27b16 100644 --- a/main/reference/range_noinf.html +++ b/main/reference/range_noinf.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html index 3369960273..c35e1c50c9 100644 --- a/main/reference/reapply_varlabels.html +++ b/main/reference/reapply_varlabels.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html index 53816fe9b9..4c72961f2a 100644 --- a/main/reference/response_biomarkers_subgroups.html +++ b/main/reference/response_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html index fb6d448883..a3f14ce1b6 100644 --- a/main/reference/response_subgroups.html +++ b/main/reference/response_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -171,7 +141,7 @@ Arguments Value An rtables table summarizing binary response by subgroup. -a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -182,8 +152,8 @@ DetailsFunctions tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups(). diff --git a/main/reference/rtable2gg.html b/main/reference/rtable2gg.html index 6068cb1bb8..cdf46a633f 100644 --- a/main/reference/rtable2gg.html +++ b/main/reference/rtable2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,7 +51,7 @@ -Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored. diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html index 0f6db1ef4c..67c8394ca2 100644 --- a/main/reference/rtables_access.html +++ b/main/reference/rtables_access.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html index 2198ad15a7..445c4fd861 100644 --- a/main/reference/s_cox_multivariate.html +++ b/main/reference/s_cox_multivariate.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html index fa683b677a..8c4b03b9ab 100644 --- a/main/reference/sas_na.html +++ b/main/reference/sas_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html index f5b86fe70a..3815c64608 100644 --- a/main/reference/score_occurrences.html +++ b/main/reference/score_occurrences.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Functionsrtables::cont_n_onecol() and rtables::cont_n_allcols(). +specified columns. Note that this is extending rtables::cont_n_onecol() and rtables::cont_n_allcols(). See also diff --git a/main/reference/split_cols_by_groups.html b/main/reference/split_cols_by_groups.html index 3c97f5cfee..f7bfcad622 100644 --- a/main/reference/split_cols_by_groups.html +++ b/main/reference/split_cols_by_groups.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -107,7 +77,7 @@ Arguments... -additional arguments to rtables::split_cols_by() in order. For instance, to +additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all). @@ -119,7 +89,7 @@ Value See also - rtables::split_cols_by() + rtables::split_cols_by() diff --git a/main/reference/split_text_grob.html b/main/reference/split_text_grob.html index 8671c0d8d3..062cbc39d2 100644 --- a/main/reference/split_text_grob.html +++ b/main/reference/split_text_grob.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html index fb97a58911..6f44c5774b 100644 --- a/main/reference/stack_grobs.html +++ b/main/reference/stack_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html index ffa8368b70..c31e318dd4 100644 --- a/main/reference/stat_mean_ci.html +++ b/main/reference/stat_mean_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html index ef90542a4e..6663e7ee57 100644 --- a/main/reference/stat_mean_pval.html +++ b/main/reference/stat_mean_pval.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_median_ci.html b/main/reference/stat_median_ci.html index ea5d28e072..f43024d65c 100644 --- a/main/reference/stat_median_ci.html +++ b/main/reference/stat_median_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_propdiff_ci.html b/main/reference/stat_propdiff_ci.html index b7119ac644..5db044c950 100644 --- a/main/reference/stat_propdiff_ci.html +++ b/main/reference/stat_propdiff_ci.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -141,7 +111,7 @@ Value See also - Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() + Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout. diff --git a/main/reference/strata_normal_quantile.html b/main/reference/strata_normal_quantile.html index afc45b01e1..82d6de6e3f 100644 --- a/main/reference/strata_normal_quantile.html +++ b/main/reference/strata_normal_quantile.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/study_arm.html b/main/reference/study_arm.html index 4a60dcedf5..67999fc7aa 100644 --- a/main/reference/study_arm.html +++ b/main/reference/study_arm.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/summarize_ancova.html b/main/reference/summarize_ancova.html index 1bf16f8b79..1eba34c24c 100644 --- a/main/reference/summarize_ancova.html +++ b/main/reference/summarize_ancova.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -254,7 +224,7 @@ ArgumentsValue summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout. s_ancova() returns a named list of 5 statistics:n: Count of complete sample size for the group. lsmean: Estimated marginal means in the group. @@ -264,13 +234,13 @@ Value pval: p-value (not adjusted for multiple comparisons). -a_ancova() returns the corresponding list with formatted rtables::CellValue(). +a_ancova() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_ancova(): Statistics function that produces a named list of results of the investigated linear model. a_ancova(): Formatted analysis function which is used as afun in summarize_ancova(). diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html index 70d34fcade..83fc422b89 100644 --- a/main/reference/summarize_change.html +++ b/main/reference/summarize_change.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -192,16 +162,16 @@ ArgumentsValue summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout. s_change_from_baseline() returns the same values returned by s_summary.numeric(). -a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits. a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change(). diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html index 5f7d10a3ef..2caae0ef34 100644 --- a/main/reference/summarize_colvars.html +++ b/main/reference/summarize_colvars.html @@ -2,13 +2,13 @@ Summarize variables in columns — summarize_colvars • tern @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -91,8 +61,8 @@ The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars(). -The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars(). +The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function. @@ -147,13 +117,13 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. See also - rtables::split_cols_by_multivar() and analyze_colvars_functions. + rtables::split_cols_by_multivar() and analyze_colvars_functions. diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html index 751617921c..34ebaf2237 100644 --- a/main/reference/summarize_functions.html +++ b/main/reference/summarize_functions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -78,7 +48,7 @@ - These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions + These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout: @@ -87,7 +57,7 @@ Details add_rowcounts() -estimate_multinomial_response() (with rtables::analyze()) +estimate_multinomial_response() (with rtables::analyze()) h_tab_one_biomarker() (probably to deprecate) logistic_summary_by_flag() summarize_num_patients() @@ -95,15 +65,15 @@ Detailssummarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() -Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table.
lyt <- basic_table() %>% - split_cols_by(var = "ARM") %>% - add_overall_col("All Patients") %>% - add_colcounts() %>% +lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients(): -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred. a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -236,17 +206,17 @@ ArgumentsValue count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout. s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag. -a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue(). Functions count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE. a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,16 +191,16 @@ ArgumentsValue count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout. s_count_values() returns output of s_summary() for specified values of a non-numeric variable. -a_count_values() returns the corresponding list with formatted rtables::CellValue(). +a_count_values() returns the corresponding list with formatted rtables::CellValue(). Functions count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_values(): S3 generic function to count values. s_count_values(character): Method for character class. s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -212,7 +182,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -238,10 +208,10 @@ ArgumentsValue summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout. s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s). -a_coxreg() returns formatted rtables::CellValue(). +a_coxreg() returns formatted rtables::CellValue(). Details @@ -257,11 +227,11 @@ Functionsrtables::analyze_colvars() and rtables::summarize_row_groups(). +is a wrapper for rtables::analyze_colvars() and rtables::summarize_row_groups(). s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list. -a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg(). +a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg(). See also diff --git a/main/reference/cox_regression_inter.html b/main/reference/cox_regression_inter.html index 6124a8f4c1..b15b4c925c 100644 --- a/main/reference/cox_regression_inter.html +++ b/main/reference/cox_regression_inter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -210,7 +180,7 @@ Note Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table(). +variables before passing the dataset to rtables::build_table(). diff --git a/main/reference/cut_quantile_bins.html b/main/reference/cut_quantile_bins.html index 976e7c3dcc..c3759b2a0d 100644 --- a/main/reference/cut_quantile_bins.html +++ b/main/reference/cut_quantile_bins.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_abnormal_by_baseline.html b/main/reference/d_count_abnormal_by_baseline.html index 5b9e065ed8..9cba629d12 100644 --- a/main/reference/d_count_abnormal_by_baseline.html +++ b/main/reference/d_count_abnormal_by_baseline.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_cumulative.html b/main/reference/d_count_cumulative.html index 8f5e09613a..ed30a89198 100644 --- a/main/reference/d_count_cumulative.html +++ b/main/reference/d_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_count_missed_doses.html b/main/reference/d_count_missed_doses.html index 3983179580..88d7cb115f 100644 --- a/main/reference/d_count_missed_doses.html +++ b/main/reference/d_count_missed_doses.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_onco_rsp_label.html b/main/reference/d_onco_rsp_label.html index 6ad11d55b2..36eb740b33 100644 --- a/main/reference/d_onco_rsp_label.html +++ b/main/reference/d_onco_rsp_label.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_pkparam.html b/main/reference/d_pkparam.html index 50dd3d760f..911054124b 100644 --- a/main/reference/d_pkparam.html +++ b/main/reference/d_pkparam.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion.html b/main/reference/d_proportion.html index 45eecb0d3c..8a8f84d672 100644 --- a/main/reference/d_proportion.html +++ b/main/reference/d_proportion.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_proportion_diff.html b/main/reference/d_proportion_diff.html index 513d2d47ae..fc4b954e11 100644 --- a/main/reference/d_proportion_diff.html +++ b/main/reference/d_proportion_diff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_rsp_subgroups_colvars.html b/main/reference/d_rsp_subgroups_colvars.html index 953122b65f..cdcbc5e18c 100644 --- a/main/reference/d_rsp_subgroups_colvars.html +++ b/main/reference/d_rsp_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_survival_subgroups_colvars.html b/main/reference/d_survival_subgroups_colvars.html index 8974168b2e..ad93487971 100644 --- a/main/reference/d_survival_subgroups_colvars.html +++ b/main/reference/d_survival_subgroups_colvars.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/d_test_proportion_diff.html b/main/reference/d_test_proportion_diff.html index f32963f964..3c31b90c80 100644 --- a/main/reference/d_test_proportion_diff.html +++ b/main/reference/d_test_proportion_diff.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/day2month.html b/main/reference/day2month.html index 4cdf9eb8d3..00a2059911 100644 --- a/main/reference/day2month.html +++ b/main/reference/day2month.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob.html b/main/reference/decorate_grob.html index 05bab36561..e0ee545cc9 100644 --- a/main/reference/decorate_grob.html +++ b/main/reference/decorate_grob.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_factory.html b/main/reference/decorate_grob_factory.html index bf15634600..c128f2d675 100644 --- a/main/reference/decorate_grob_factory.html +++ b/main/reference/decorate_grob_factory.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/decorate_grob_set.html b/main/reference/decorate_grob_set.html index 5193dd3c1b..0de85e9334 100644 --- a/main/reference/decorate_grob_set.html +++ b/main/reference/decorate_grob_set.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_na_str.html b/main/reference/default_na_str.html index 53dc58920e..406deecf68 100644 --- a/main/reference/default_na_str.html +++ b/main/reference/default_na_str.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/default_stats_formats_labels.html b/main/reference/default_stats_formats_labels.html index 9969f93d9f..f98b60bff5 100644 --- a/main/reference/default_stats_formats_labels.html +++ b/main/reference/default_stats_formats_labels.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,7 +193,7 @@ Note These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings. Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables"). +return a string. This is well documented in vignette("custom_appearance", package = "rtables"). See also diff --git a/main/reference/desctools_binom.html b/main/reference/desctools_binom.html index 4b01634fe0..6be9e52e5c 100644 --- a/main/reference/desctools_binom.html +++ b/main/reference/desctools_binom.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df2gg.html b/main/reference/df2gg.html index d2a0c06ec1..3459218bc2 100644 --- a/main/reference/df2gg.html +++ b/main/reference/df2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/df_explicit_na.html b/main/reference/df_explicit_na.html index 7239b2d0e5..3e4a639463 100644 --- a/main/reference/df_explicit_na.html +++ b/main/reference/df_explicit_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/dot-is_equal_float.html b/main/reference/dot-is_equal_float.html index 0e13761934..6c1e53f091 100644 --- a/main/reference/dot-is_equal_float.html +++ b/main/reference/dot-is_equal_float.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/draw_grob.html b/main/reference/draw_grob.html index 451b8e7fd6..f6cdcb0a01 100644 --- a/main/reference/draw_grob.html +++ b/main/reference/draw_grob.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/empty_vector_if_na.html b/main/reference/empty_vector_if_na.html index 7607d61187..452a306105 100644 --- a/main/reference/empty_vector_if_na.html +++ b/main/reference/empty_vector_if_na.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_coef.html b/main/reference/estimate_coef.html index 2bc5525f87..ce5485013a 100644 --- a/main/reference/estimate_coef.html +++ b/main/reference/estimate_coef.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/estimate_multinomial_rsp.html b/main/reference/estimate_multinomial_rsp.html index c917481057..4960eb095e 100644 --- a/main/reference/estimate_multinomial_rsp.html +++ b/main/reference/estimate_multinomial_rsp.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -177,17 +147,17 @@ ArgumentsValue estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout. s_length_proportion() returns statistics from s_proportion(). -a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_length_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::analyze() and +rtables::summarize_row_groups(). s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion(). a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -244,17 +214,17 @@ ArgumentsValue estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout. s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable. -a_proportion() returns the corresponding list with formatted rtables::CellValue(). +a_proportion() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion(): Statistics function estimating a proportion along with its confidence interval. a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/explicit_na.html b/main/reference/explicit_na.html index a19da4827a..75158ed039 100644 --- a/main/reference/explicit_na.html +++ b/main/reference/explicit_na.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_by_name.html b/main/reference/extract_by_name.html index 890e00c7b2..7f4ea02325 100644 --- a/main/reference/extract_by_name.html +++ b/main/reference/extract_by_name.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_biomarkers.html b/main/reference/extract_rsp_biomarkers.html index 47f4183173..22d0b045c8 100644 --- a/main/reference/extract_rsp_biomarkers.html +++ b/main/reference/extract_rsp_biomarkers.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_rsp_subgroups.html b/main/reference/extract_rsp_subgroups.html index 10e7fd68dd..bca8059217 100644 --- a/main/reference/extract_rsp_subgroups.html +++ b/main/reference/extract_rsp_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_biomarkers.html b/main/reference/extract_survival_biomarkers.html index 4756961aa7..353e5cea60 100644 --- a/main/reference/extract_survival_biomarkers.html +++ b/main/reference/extract_survival_biomarkers.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extract_survival_subgroups.html b/main/reference/extract_survival_subgroups.html index 825bc1eedd..fc3a8da8f2 100644 --- a/main/reference/extract_survival_subgroups.html +++ b/main/reference/extract_survival_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/extreme_format.html b/main/reference/extreme_format.html index cfb5075e63..c363e0dcb0 100644 --- a/main/reference/extreme_format.html +++ b/main/reference/extreme_format.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_conf_level.html b/main/reference/f_conf_level.html index a2fa7874b0..e59e2a03ae 100644 --- a/main/reference/f_conf_level.html +++ b/main/reference/f_conf_level.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/f_pval.html b/main/reference/f_pval.html index 1c4fea37df..5d7ab493ce 100644 --- a/main/reference/f_pval.html +++ b/main/reference/f_pval.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_collapse_only.html b/main/reference/fct_collapse_only.html index 83ab24eb82..f332240368 100644 --- a/main/reference/fct_collapse_only.html +++ b/main/reference/fct_collapse_only.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_discard.html b/main/reference/fct_discard.html index d7fb3b6c64..4cac1ccba5 100644 --- a/main/reference/fct_discard.html +++ b/main/reference/fct_discard.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fct_explicit_na_if.html b/main/reference/fct_explicit_na_if.html index 3bcaf94176..6848982dde 100644 --- a/main/reference/fct_explicit_na_if.html +++ b/main/reference/fct_explicit_na_if.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_coxreg.html b/main/reference/fit_coxreg.html index 14c5d95f6b..5c2bfc26a0 100644 --- a/main/reference/fit_coxreg.html +++ b/main/reference/fit_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_logistic.html b/main/reference/fit_logistic.html index 0f364c07c5..4e6119b249 100644 --- a/main/reference/fit_logistic.html +++ b/main/reference/fit_logistic.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_rsp_step.html b/main/reference/fit_rsp_step.html index f5d42bfe7c..5f64d2f786 100644 --- a/main/reference/fit_rsp_step.html +++ b/main/reference/fit_rsp_step.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/fit_survival_step.html b/main/reference/fit_survival_step.html index b7da54c090..ca68ad9a04 100644 --- a/main/reference/fit_survival_step.html +++ b/main/reference/fit_survival_step.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/forest_viewport.html b/main/reference/forest_viewport.html index 2e578ce576..964ab27f65 100644 --- a/main/reference/forest_viewport.html +++ b/main/reference/forest_viewport.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_auto.html b/main/reference/format_auto.html index 66cff1543b..f5b5ca5633 100644 --- a/main/reference/format_auto.html +++ b/main/reference/format_auto.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -106,7 +76,7 @@ Argumentsdt_var (numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered. diff --git a/main/reference/format_count_fraction.html b/main/reference/format_count_fraction.html index 7c89de6365..4559bb997c 100644 --- a/main/reference/format_count_fraction.html +++ b/main/reference/format_count_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_fixed_dp.html b/main/reference/format_count_fraction_fixed_dp.html index fb67db8371..ae6e985489 100644 --- a/main/reference/format_count_fraction_fixed_dp.html +++ b/main/reference/format_count_fraction_fixed_dp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_count_fraction_lt10.html b/main/reference/format_count_fraction_lt10.html index 9c0e167253..ce2f2e82e2 100644 --- a/main/reference/format_count_fraction_lt10.html +++ b/main/reference/format_count_fraction_lt10.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values.html b/main/reference/format_extreme_values.html index c12a059e98..6fdb3dd26f 100644 --- a/main/reference/format_extreme_values.html +++ b/main/reference/format_extreme_values.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_extreme_values_ci.html b/main/reference/format_extreme_values_ci.html index dea1549bfe..c60b369a7d 100644 --- a/main/reference/format_extreme_values_ci.html +++ b/main/reference/format_extreme_values_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction.html b/main/reference/format_fraction.html index 8d4274cffb..2c2145230d 100644 --- a/main/reference/format_fraction.html +++ b/main/reference/format_fraction.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_fixed_dp.html b/main/reference/format_fraction_fixed_dp.html index 297df429d5..3c5ba714ad 100644 --- a/main/reference/format_fraction_fixed_dp.html +++ b/main/reference/format_fraction_fixed_dp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_fraction_threshold.html b/main/reference/format_fraction_threshold.html index fc7c8ddea7..def9b6a40e 100644 --- a/main/reference/format_fraction_threshold.html +++ b/main/reference/format_fraction_threshold.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_sigfig.html b/main/reference/format_sigfig.html index 02a2b8591e..692c3a6abc 100644 --- a/main/reference/format_sigfig.html +++ b/main/reference/format_sigfig.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/format_xx.html b/main/reference/format_xx.html index 0ed5c9a3d8..18bbe09129 100644 --- a/main/reference/format_xx.html +++ b/main/reference/format_xx.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/formatting_functions.html b/main/reference/formatting_functions.html index d325263a97..13af4631b1 100644 --- a/main/reference/formatting_functions.html +++ b/main/reference/formatting_functions.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_forest.html b/main/reference/g_forest.html index 3c11db2339..90308fb9b9 100644 --- a/main/reference/g_forest.html +++ b/main/reference/g_forest.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -221,7 +191,7 @@ Value Details - Given a rtables::rtable() object with at least one column with a single value and one column with 2 + Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side. diff --git a/main/reference/g_ipp.html b/main/reference/g_ipp.html index a0fe570f3e..187580ab44 100644 --- a/main/reference/g_ipp.html +++ b/main/reference/g_ipp.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_km.html b/main/reference/g_km.html index cdfc1967d3..f352196584 100644 --- a/main/reference/g_km.html +++ b/main/reference/g_km.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_lineplot.html b/main/reference/g_lineplot.html index 19a73c87f9..f690b652d5 100644 --- a/main/reference/g_lineplot.html +++ b/main/reference/g_lineplot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_step.html b/main/reference/g_step.html index c610853a23..eb4f82b05a 100644 --- a/main/reference/g_step.html +++ b/main/reference/g_step.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/g_waterfall.html b/main/reference/g_waterfall.html index cfd51f294f..de2d4f49d4 100644 --- a/main/reference/g_waterfall.html +++ b/main/reference/g_waterfall.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_covariates.html b/main/reference/get_covariates.html index f17bf37bfe..ed76800bad 100644 --- a/main/reference/get_covariates.html +++ b/main/reference/get_covariates.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/get_smooths.html b/main/reference/get_smooths.html index 6214ffa6c9..6215eb9b9d 100644 --- a/main/reference/get_smooths.html +++ b/main/reference/get_smooths.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/groups_list_to_df.html b/main/reference/groups_list_to_df.html index 6e3a623ff0..b5b046e9bb 100644 --- a/main/reference/groups_list_to_df.html +++ b/main/reference/groups_list_to_df.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -76,7 +46,7 @@ - This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). + This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels(). diff --git a/main/reference/h_adlb_abnormal_by_worst_grade.html b/main/reference/h_adlb_abnormal_by_worst_grade.html index 924b3126fd..124a0f60f6 100644 --- a/main/reference/h_adlb_abnormal_by_worst_grade.html +++ b/main/reference/h_adlb_abnormal_by_worst_grade.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adlb_worsen.html b/main/reference/h_adlb_worsen.html index c86690c603..a1da8de34a 100644 --- a/main/reference/h_adlb_worsen.html +++ b/main/reference/h_adlb_worsen.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_adsl_adlb_merge_using_worst_flag.html b/main/reference/h_adsl_adlb_merge_using_worst_flag.html index 5fc0bf7381..b1b901e4e0 100644 --- a/main/reference/h_adsl_adlb_merge_using_worst_flag.html +++ b/main/reference/h_adsl_adlb_merge_using_worst_flag.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ancova.html b/main/reference/h_ancova.html index 27bf03975f..59f11617c8 100644 --- a/main/reference/h_ancova.html +++ b/main/reference/h_ancova.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_append_grade_groups.html b/main/reference/h_append_grade_groups.html index 2776a060c9..40ba81d7bd 100644 --- a/main/reference/h_append_grade_groups.html +++ b/main/reference/h_append_grade_groups.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_col_indices.html b/main/reference/h_col_indices.html index 06b97cf510..cbaecea676 100644 --- a/main/reference/h_col_indices.html +++ b/main/reference/h_col_indices.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_count_cumulative.html b/main/reference/h_count_cumulative.html index 36c0c9a1e1..5d58afe576 100644 --- a/main/reference/h_count_cumulative.html +++ b/main/reference/h_count_cumulative.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_cox_regression.html b/main/reference/h_cox_regression.html index 4be3ea8680..2300696d50 100644 --- a/main/reference/h_cox_regression.html +++ b/main/reference/h_cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_data_plot.html b/main/reference/h_data_plot.html index 7ac8b43b49..54b0d7080d 100644 --- a/main/reference/h_data_plot.html +++ b/main/reference/h_data_plot.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_decompose_gg.html b/main/reference/h_decompose_gg.html index 4cf323ad4e..96502e49d1 100644 --- a/main/reference/h_decompose_gg.html +++ b/main/reference/h_decompose_gg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_format_row.html b/main/reference/h_format_row.html index 1033ff5a0c..11071684bb 100644 --- a/main/reference/h_format_row.html +++ b/main/reference/h_format_row.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -95,7 +65,7 @@ Argumentsformat (named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter. diff --git a/main/reference/h_g_ipp.html b/main/reference/h_g_ipp.html index ba609f3d69..d4c8203cba 100644 --- a/main/reference/h_g_ipp.html +++ b/main/reference/h_g_ipp.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ggkm.html b/main/reference/h_ggkm.html index 512f6a9180..0aac0f9127 100644 --- a/main/reference/h_ggkm.html +++ b/main/reference/h_ggkm.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_glm_count.html b/main/reference/h_glm_count.html index 036db54acf..74a4e7900b 100644 --- a/main/reference/h_glm_count.html +++ b/main/reference/h_glm_count.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_coxph.html b/main/reference/h_grob_coxph.html index 7dcead7d01..132d15c26a 100644 --- a/main/reference/h_grob_coxph.html +++ b/main/reference/h_grob_coxph.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_median_surv.html b/main/reference/h_grob_median_surv.html index 82ea2d549f..60bd8ff8d5 100644 --- a/main/reference/h_grob_median_surv.html +++ b/main/reference/h_grob_median_surv.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_tbl_at_risk.html b/main/reference/h_grob_tbl_at_risk.html index 3af28455fb..7879a81306 100644 --- a/main/reference/h_grob_tbl_at_risk.html +++ b/main/reference/h_grob_tbl_at_risk.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_grob_y_annot.html b/main/reference/h_grob_y_annot.html index 48a9227ad3..dd46627d90 100644 --- a/main/reference/h_grob_y_annot.html +++ b/main/reference/h_grob_y_annot.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_incidence_rate.html b/main/reference/h_incidence_rate.html index 77eb4bd003..e85621d716 100644 --- a/main/reference/h_incidence_rate.html +++ b/main/reference/h_incidence_rate.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_km_layout.html b/main/reference/h_km_layout.html index 9e3fdc86ed..67d651af4a 100644 --- a/main/reference/h_km_layout.html +++ b/main/reference/h_km_layout.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_logistic_regression.html b/main/reference/h_logistic_regression.html index fe6cbfe730..8dd16febea 100644 --- a/main/reference/h_logistic_regression.html +++ b/main/reference/h_logistic_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_map_for_count_abnormal.html b/main/reference/h_map_for_count_abnormal.html index 10495898fc..59f19b1f76 100644 --- a/main/reference/h_map_for_count_abnormal.html +++ b/main/reference/h_map_for_count_abnormal.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_odds_ratio.html b/main/reference/h_odds_ratio.html index e1e17eedc3..b93837e390 100644 --- a/main/reference/h_odds_ratio.html +++ b/main/reference/h_odds_ratio.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_pkparam_sort.html b/main/reference/h_pkparam_sort.html index 982b2989b6..318bbf2ecc 100644 --- a/main/reference/h_pkparam_sort.html +++ b/main/reference/h_pkparam_sort.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_ppmeans.html b/main/reference/h_ppmeans.html index 0ce7208de8..57fe7b7773 100644 --- a/main/reference/h_ppmeans.html +++ b/main/reference/h_ppmeans.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff.html b/main/reference/h_prop_diff.html index 82c09a19d9..df5cd7a011 100644 --- a/main/reference/h_prop_diff.html +++ b/main/reference/h_prop_diff.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_prop_diff_test.html b/main/reference/h_prop_diff_test.html index 879561bbee..7ebaf8c228 100644 --- a/main/reference/h_prop_diff_test.html +++ b/main/reference/h_prop_diff_test.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_proportions.html b/main/reference/h_proportions.html index e2454b4cec..cae1aa23c0 100644 --- a/main/reference/h_proportions.html +++ b/main/reference/h_proportions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_biomarkers_subgroups.html b/main/reference/h_response_biomarkers_subgroups.html index 6253742dc3..d40c44e05a 100644 --- a/main/reference/h_response_biomarkers_subgroups.html +++ b/main/reference/h_response_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_response_subgroups.html b/main/reference/h_response_subgroups.html index f8553643c3..3277639d7f 100644 --- a/main/reference/h_response_subgroups.html +++ b/main/reference/h_response_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_by_subgroups.html b/main/reference/h_split_by_subgroups.html index 30b5234a3f..53bbd7df94 100644 --- a/main/reference/h_split_by_subgroups.html +++ b/main/reference/h_split_by_subgroups.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_split_param.html b/main/reference/h_split_param.html index 1c85bd0acc..f531b50248 100644 --- a/main/reference/h_split_param.html +++ b/main/reference/h_split_param.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_stack_by_baskets.html b/main/reference/h_stack_by_baskets.html index 856a6bfda2..6f466ed562 100644 --- a/main/reference/h_stack_by_baskets.html +++ b/main/reference/h_stack_by_baskets.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_step.html b/main/reference/h_step.html index 22264810df..aed7400437 100644 --- a/main/reference/h_step.html +++ b/main/reference/h_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_biomarkers_subgroups.html b/main/reference/h_survival_biomarkers_subgroups.html index e2ef402488..24d8bdff48 100644 --- a/main/reference/h_survival_biomarkers_subgroups.html +++ b/main/reference/h_survival_biomarkers_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_survival_duration_subgroups.html b/main/reference/h_survival_duration_subgroups.html index f45ef9d1ed..27a3f5c549 100644 --- a/main/reference/h_survival_duration_subgroups.html +++ b/main/reference/h_survival_duration_subgroups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tab_one_biomarker.html b/main/reference/h_tab_one_biomarker.html index ca44867180..75abc5d17b 100644 --- a/main/reference/h_tab_one_biomarker.html +++ b/main/reference/h_tab_one_biomarker.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups(). +This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/h_tbl_coxph_pairwise.html b/main/reference/h_tbl_coxph_pairwise.html index fb85cb68ff..d06f1ec802 100644 --- a/main/reference/h_tbl_coxph_pairwise.html +++ b/main/reference/h_tbl_coxph_pairwise.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_tbl_median_surv.html b/main/reference/h_tbl_median_surv.html index 824c9b6ba7..6c36b258c8 100644 --- a/main/reference/h_tbl_median_surv.html +++ b/main/reference/h_tbl_median_surv.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_worsen_counter.html b/main/reference/h_worsen_counter.html index bd3da774c9..75cee2d4f8 100644 --- a/main/reference/h_worsen_counter.html +++ b/main/reference/h_worsen_counter.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/h_xticks.html b/main/reference/h_xticks.html index 3b00b0578e..cdca04f44b 100644 --- a/main/reference/h_xticks.html +++ b/main/reference/h_xticks.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/imputation_rule.html b/main/reference/imputation_rule.html index 4af234fef7..9793443aea 100644 --- a/main/reference/imputation_rule.html +++ b/main/reference/imputation_rule.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/incidence_rate.html b/main/reference/incidence_rate.html index 54c857ff9c..c6a5c87c16 100644 --- a/main/reference/incidence_rate.html +++ b/main/reference/incidence_rate.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -239,7 +209,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -251,7 +221,7 @@ ArgumentsValue estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout. s_incidence_rate() returns the following statistics:person_years: Total person-years at risk. n_events: Total number of events observed. @@ -260,13 +230,13 @@ Valuen_unique: Total number of patients with at least one event observed. n_rate: Total number of events observed & estimated incidence rate. -a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). +a_incidence_rate() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval. a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate(). diff --git a/main/reference/index.html b/main/reference/index.html index 2a1665f99f..09b3124477 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -196,7 +166,7 @@ Analysis FunctionsAnalyze functions with their corresponding statistics functions and formatted analysis functions.Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table. Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves. -Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). +Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze(). diff --git a/main/reference/labels_or_names.html b/main/reference/labels_or_names.html index 3f1016c509..dd93cf81ce 100644 --- a/main/reference/labels_or_names.html +++ b/main/reference/labels_or_names.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/labels_use_control.html b/main/reference/labels_use_control.html index 5c301b4228..d4006f99f7 100644 --- a/main/reference/labels_use_control.html +++ b/main/reference/labels_use_control.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/logistic_regression_cols.html b/main/reference/logistic_regression_cols.html index 7744090684..8accd2e89b 100644 --- a/main/reference/logistic_regression_cols.html +++ b/main/reference/logistic_regression_cols.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar(). +regression results. This function is a wrapper for rtables::split_cols_by_multivar(). diff --git a/main/reference/logistic_summary_by_flag.html b/main/reference/logistic_summary_by_flag.html index 9807a24c22..45cb84b416 100644 --- a/main/reference/logistic_summary_by_flag.html +++ b/main/reference/logistic_summary_by_flag.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -82,7 +52,7 @@ Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). +logistic regression results. This function is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/make_names.html b/main/reference/make_names.html index d14c4af69d..ef3f426440 100644 --- a/main/reference/make_names.html +++ b/main/reference/make_names.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/month2day.html b/main/reference/month2day.html index 87004ac14c..bb59b07ac8 100644 --- a/main/reference/month2day.html +++ b/main/reference/month2day.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/muffled_car_anova.html b/main/reference/muffled_car_anova.html index 3d902766c3..e4c73d2ed5 100644 --- a/main/reference/muffled_car_anova.html +++ b/main/reference/muffled_car_anova.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/n_available.html b/main/reference/n_available.html index af70552f25..d8856b8358 100644 --- a/main/reference/n_available.html +++ b/main/reference/n_available.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/odds_ratio.html b/main/reference/odds_ratio.html index 3bcb51179d..58a1801d4a 100644 --- a/main/reference/odds_ratio.html +++ b/main/reference/odds_ratio.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -228,17 +198,17 @@ ArgumentsValue estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout. s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot. -a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). +a_odds_ratio() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required. diff --git a/main/reference/prop_diff.html b/main/reference/prop_diff.html index 51e9d86731..bd76dd200f 100644 --- a/main/reference/prop_diff.html +++ b/main/reference/prop_diff.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -237,16 +207,16 @@ ArgumentsValue estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout. s_proportion_diff() returns a named list of elements diff and diff_ci. -a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion. a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff(). diff --git a/main/reference/prop_diff_test.html b/main/reference/prop_diff_test.html index 17bf534958..d4bfbab57f 100644 --- a/main/reference/prop_diff_test.html +++ b/main/reference/prop_diff_test.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -217,17 +187,17 @@ ArgumentsValue test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout. s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same. -a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). +a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue(). Functions test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_test_proportion_diff(): Statistics function which tests the difference between two proportions. a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff(). diff --git a/main/reference/prune_occurrences.html b/main/reference/prune_occurrences.html index 74e764167c..75f049f5b1 100644 --- a/main/reference/prune_occurrences.html +++ b/main/reference/prune_occurrences.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -137,7 +107,7 @@ Arguments Value -keep_rows() returns a pruning function that can be used with rtables::prune_table() +keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table. keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table. @@ -180,7 +150,7 @@ FunctionsNote Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface. +should be pruned, to conform with the rtables::prune_table() interface. diff --git a/main/reference/range_noinf.html b/main/reference/range_noinf.html index ccf69253f9..8bcad27b16 100644 --- a/main/reference/range_noinf.html +++ b/main/reference/range_noinf.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/reapply_varlabels.html b/main/reference/reapply_varlabels.html index 3369960273..c35e1c50c9 100644 --- a/main/reference/reapply_varlabels.html +++ b/main/reference/reapply_varlabels.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_biomarkers_subgroups.html b/main/reference/response_biomarkers_subgroups.html index 53816fe9b9..4c72961f2a 100644 --- a/main/reference/response_biomarkers_subgroups.html +++ b/main/reference/response_biomarkers_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/response_subgroups.html b/main/reference/response_subgroups.html index fb6d448883..a3f14ce1b6 100644 --- a/main/reference/response_subgroups.html +++ b/main/reference/response_subgroups.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -171,7 +141,7 @@ Arguments Value An rtables table summarizing binary response by subgroup. -a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). +a_response_subgroups() returns the corresponding list with formatted rtables::CellValue(). Details @@ -182,8 +152,8 @@ DetailsFunctions tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups(). +summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() +and rtables::summarize_row_groups(). a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups(). diff --git a/main/reference/rtable2gg.html b/main/reference/rtable2gg.html index 6068cb1bb8..cdf46a633f 100644 --- a/main/reference/rtable2gg.html +++ b/main/reference/rtable2gg.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,7 +51,7 @@ -Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored. diff --git a/main/reference/rtables_access.html b/main/reference/rtables_access.html index 0f6db1ef4c..67c8394ca2 100644 --- a/main/reference/rtables_access.html +++ b/main/reference/rtables_access.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/s_cox_multivariate.html b/main/reference/s_cox_multivariate.html index 2198ad15a7..445c4fd861 100644 --- a/main/reference/s_cox_multivariate.html +++ b/main/reference/s_cox_multivariate.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/sas_na.html b/main/reference/sas_na.html index fa683b677a..8c4b03b9ab 100644 --- a/main/reference/sas_na.html +++ b/main/reference/sas_na.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/score_occurrences.html b/main/reference/score_occurrences.html index f5b86fe70a..3815c64608 100644 --- a/main/reference/score_occurrences.html +++ b/main/reference/score_occurrences.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Functionsrtables::cont_n_onecol() and rtables::cont_n_allcols(). +specified columns. Note that this is extending rtables::cont_n_onecol() and rtables::cont_n_allcols(). See also diff --git a/main/reference/split_cols_by_groups.html b/main/reference/split_cols_by_groups.html index 3c97f5cfee..f7bfcad622 100644 --- a/main/reference/split_cols_by_groups.html +++ b/main/reference/split_cols_by_groups.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -107,7 +77,7 @@ Arguments... -additional arguments to rtables::split_cols_by() in order. For instance, to +additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all). @@ -119,7 +89,7 @@ Value See also - rtables::split_cols_by() + rtables::split_cols_by() diff --git a/main/reference/split_text_grob.html b/main/reference/split_text_grob.html index 8671c0d8d3..062cbc39d2 100644 --- a/main/reference/split_text_grob.html +++ b/main/reference/split_text_grob.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stack_grobs.html b/main/reference/stack_grobs.html index fb97a58911..6f44c5774b 100644 --- a/main/reference/stack_grobs.html +++ b/main/reference/stack_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_ci.html b/main/reference/stat_mean_ci.html index ffa8368b70..c31e318dd4 100644 --- a/main/reference/stat_mean_ci.html +++ b/main/reference/stat_mean_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_mean_pval.html b/main/reference/stat_mean_pval.html index ef90542a4e..6663e7ee57 100644 --- a/main/reference/stat_mean_pval.html +++ b/main/reference/stat_mean_pval.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_median_ci.html b/main/reference/stat_median_ci.html index ea5d28e072..f43024d65c 100644 --- a/main/reference/stat_median_ci.html +++ b/main/reference/stat_median_ci.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/stat_propdiff_ci.html b/main/reference/stat_propdiff_ci.html index b7119ac644..5db044c950 100644 --- a/main/reference/stat_propdiff_ci.html +++ b/main/reference/stat_propdiff_ci.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -141,7 +111,7 @@ Value See also - Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() + Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout. diff --git a/main/reference/strata_normal_quantile.html b/main/reference/strata_normal_quantile.html index afc45b01e1..82d6de6e3f 100644 --- a/main/reference/strata_normal_quantile.html +++ b/main/reference/strata_normal_quantile.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/study_arm.html b/main/reference/study_arm.html index 4a60dcedf5..67999fc7aa 100644 --- a/main/reference/study_arm.html +++ b/main/reference/study_arm.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/summarize_ancova.html b/main/reference/summarize_ancova.html index 1bf16f8b79..1eba34c24c 100644 --- a/main/reference/summarize_ancova.html +++ b/main/reference/summarize_ancova.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -254,7 +224,7 @@ ArgumentsValue summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout. s_ancova() returns a named list of 5 statistics:n: Count of complete sample size for the group. lsmean: Estimated marginal means in the group. @@ -264,13 +234,13 @@ Value pval: p-value (not adjusted for multiple comparisons). -a_ancova() returns the corresponding list with formatted rtables::CellValue(). +a_ancova() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_ancova(): Statistics function that produces a named list of results of the investigated linear model. a_ancova(): Formatted analysis function which is used as afun in summarize_ancova(). diff --git a/main/reference/summarize_change.html b/main/reference/summarize_change.html index 70d34fcade..83fc422b89 100644 --- a/main/reference/summarize_change.html +++ b/main/reference/summarize_change.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -192,16 +162,16 @@ ArgumentsValue summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout. s_change_from_baseline() returns the same values returned by s_summary.numeric(). -a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits. a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change(). diff --git a/main/reference/summarize_colvars.html b/main/reference/summarize_colvars.html index 5f7d10a3ef..2caae0ef34 100644 --- a/main/reference/summarize_colvars.html +++ b/main/reference/summarize_colvars.html @@ -2,13 +2,13 @@ Summarize variables in columns — summarize_colvars • tern @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -91,8 +61,8 @@ The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars(). -The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars(). +The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function. @@ -147,13 +117,13 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. See also - rtables::split_cols_by_multivar() and analyze_colvars_functions. + rtables::split_cols_by_multivar() and analyze_colvars_functions. diff --git a/main/reference/summarize_functions.html b/main/reference/summarize_functions.html index 751617921c..34ebaf2237 100644 --- a/main/reference/summarize_functions.html +++ b/main/reference/summarize_functions.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -78,7 +48,7 @@ - These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions + These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout: @@ -87,7 +57,7 @@ Details add_rowcounts() -estimate_multinomial_response() (with rtables::analyze()) +estimate_multinomial_response() (with rtables::analyze()) h_tab_one_biomarker() (probably to deprecate) logistic_summary_by_flag() summarize_num_patients() @@ -95,15 +65,15 @@ Detailssummarize_occurrences_by_grade() summarize_patients_events_in_cols() summarize_patients_exposure_in_cols() -Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table.
lyt <- basic_table() %>% + split_cols_by(var = "ARM") %>% + add_overall_col("All Patients") %>% + add_colcounts() %>% analyze_vars( vars = vars, var_labels = var_labels ) -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "BRA")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=7) (N=13) (N=10) (N=30) #> —————————————————————————————————————————————————————————————————————————————— @@ -396,7 +366,7 @@ Demographic Table#> U 0 0 0 0 #> UNDIFFERENTIATED 0 0 0 0 -build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) +build_table(lyt, df = adsl %>% dplyr::filter(COUNTRY == "CHN")) #> B: Placebo A: Drug X C: Combination All Patients #> (N=81) (N=74) (N=64) (N=219) #> —————————————————————————————————————————————————————————————————————————————— @@ -425,10 +395,10 @@ Adverse Event Tablesummarize_num_patients()
-basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade. a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -174,7 +144,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -187,7 +157,7 @@ ArgumentsValue summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout. s_count_patients_and_multiple_events() returns a list with the statistics:unique: number of unique patients in df. all: number of rows in df. @@ -199,7 +169,7 @@ ValueFunctions summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols(). diff --git a/main/reference/count_patients_with_event.html b/main/reference/count_patients_with_event.html index 68e6118f36..21b06cd9dc 100644 --- a/main/reference/count_patients_with_event.html +++ b/main/reference/count_patients_with_event.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -37,36 +37,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -226,16 +196,16 @@ ArgumentsValue count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout. s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event. -a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue(). +a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table(), +the alt_df_counts argument in build_table(), which provides an alternative data set for deriving the counts in the header. This is often required when we work with data sets that include multiple records per patient as df, such as @@ -508,16 +478,16 @@ Statistics Functionssummarize_num_patients()
basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -437,7 +407,7 @@ Adverse Event Table= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -449,7 +419,7 @@ Adverse Event Tablebuild_table()
alt_df_counts
build_table()
df
-basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by() before calling again +with split_rows_by() before calling again summarize_num_patients(). -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). Functions count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). +and additional format arguments. This function is a wrapper for rtables::summarize_row_groups(). s_count_occurrences(): Statistics function which counts number of patients that report an occurrence. a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -45,36 +45,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -265,7 +235,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -273,22 +243,22 @@ ArgumentsValue count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout. summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout. s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping. -a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = "unique_count", .labels = c(unique_count = "Total number of patients with at least one AE") ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -529,13 +499,13 @@ Statistics Functionssplit_rows_by()
split_rows_by()
summarize_num_patients()
-basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences(). Let’s first try using this function in a simpler layout without row splits: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%) Putting everything together, the final AE table looks like this: -basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). Functions count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds. a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative(). diff --git a/main/reference/count_missed_doses.html b/main/reference/count_missed_doses.html index ea1eacf5c1..ba448f622b 100644 --- a/main/reference/count_missed_doses.html +++ b/main/reference/count_missed_doses.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -194,17 +164,17 @@ ArgumentsValue count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout. s_count_nonmissing() returns the statistic n which is the count of non-missing values in x. s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold. -a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). +a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue(). Functions count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_nonmissing(): Statistics function to count non-missing values. s_count_missed_doses(): Statistics function to count patients with missed doses. a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -41,36 +41,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -263,7 +233,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -271,24 +241,24 @@ ArgumentsValue count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout. summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout. s_count_occurrences() returns a list with:count: list of counts with one element per occurrence. count_fraction: list of counts and fractions with one element per occurrence. fraction: list of numerators and denominators with one element per occurrence. -a_count_occurrences() returns the corresponding list with formatted rtables::CellValue(). +a_count_occurrences() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -544,7 +514,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -559,7 +529,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -595,12 +565,12 @@ Statistics Functionscount_occurrences()
-basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)
basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)
basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -619,10 +589,10 @@ Statistics Functions#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%) 162 (40.5%)
Putting everything together, the final AE table looks like this:
-basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ACTARM") %>% - add_colcounts() %>% - add_overall_col(label = "All Patients") %>% +basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion() layout creating function: -basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2) To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout. Note -This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases. In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@ Note See also - analyze_vars(), rtables::analyze_colvars(). + analyze_vars(), rtables::analyze_colvars(). diff --git a/main/reference/append_varlabels.html b/main/reference/append_varlabels.html index 8e61680a7c..83f246710c 100644 --- a/main/reference/append_varlabels.html +++ b/main/reference/append_varlabels.html @@ -13,7 +13,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -31,36 +31,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/apply_auto_formatting.html b/main/reference/apply_auto_formatting.html index 9c5b4db794..d95a2a7db7 100644 --- a/main/reference/apply_auto_formatting.html +++ b/main/reference/apply_auto_formatting.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/argument_convention.html b/main/reference/argument_convention.html index f2ca89c453..76ed5434c0 100644 --- a/main/reference/argument_convention.html +++ b/main/reference/argument_convention.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -98,7 +68,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .df_row @@ -192,7 +162,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. diff --git a/main/reference/arrange_grobs.html b/main/reference/arrange_grobs.html index de38e9467c..193b329c30 100644 --- a/main/reference/arrange_grobs.html +++ b/main/reference/arrange_grobs.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as.rtable.html b/main/reference/as.rtable.html index 6b334d428c..985351a814 100644 --- a/main/reference/as.rtable.html +++ b/main/reference/as.rtable.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/as_factor_keep_attributes.html b/main/reference/as_factor_keep_attributes.html index e95e002b2d..3b3f175e62 100644 --- a/main/reference/as_factor_keep_attributes.html +++ b/main/reference/as_factor_keep_attributes.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/assertions.html b/main/reference/assertions.html index 3dc393e366..da4f9d9992 100644 --- a/main/reference/assertions.html +++ b/main/reference/assertions.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bins_percent_labels.html b/main/reference/bins_percent_labels.html index 194fd345c8..53c13e648e 100644 --- a/main/reference/bins_percent_labels.html +++ b/main/reference/bins_percent_labels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/bland_altman.html b/main/reference/bland_altman.html index 139ac100b5..f5b226d7a1 100644 --- a/main/reference/bland_altman.html +++ b/main/reference/bland_altman.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/c_label_n.html b/main/reference/c_label_n.html index 46bc1292d3..af1d614382 100644 --- a/main/reference/c_label_n.html +++ b/main/reference/c_label_n.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -111,7 +81,7 @@ Arguments Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. Note diff --git a/main/reference/c_label_n_alt.html b/main/reference/c_label_n_alt.html index 9733c83b48..cca9ba81a1 100644 --- a/main/reference/c_label_n_alt.html +++ b/main/reference/c_label_n_alt.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,13 +70,13 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. Value - A list with formatted rtables::CellValue() with the row count value and the correct label. + A list with formatted rtables::CellValue() with the row count value and the correct label. See also diff --git a/main/reference/cfun_by_flag.html b/main/reference/cfun_by_flag.html index b20d3711e2..06cd1c0808 100644 --- a/main/reference/cfun_by_flag.html +++ b/main/reference/cfun_by_flag.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_diff_prop_ci.html b/main/reference/check_diff_prop_ci.html index e85d58750c..1bec336b0b 100644 --- a/main/reference/check_diff_prop_ci.html +++ b/main/reference/check_diff_prop_ci.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/check_same_n.html b/main/reference/check_same_n.html index 036898a27a..e5e3f52ba8 100644 --- a/main/reference/check_same_n.html +++ b/main/reference/check_same_n.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combination_function.html b/main/reference/combination_function.html index cb0a4d0846..f78a466592 100644 --- a/main/reference/combination_function.html +++ b/main/reference/combination_function.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_counts.html b/main/reference/combine_counts.html index 076602300c..e14e221f14 100644 --- a/main/reference/combine_counts.html +++ b/main/reference/combine_counts.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_groups.html b/main/reference/combine_groups.html index 91e7fae526..8dbda3f67f 100644 --- a/main/reference/combine_groups.html +++ b/main/reference/combine_groups.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_levels.html b/main/reference/combine_levels.html index 4f9903b7da..c254885d55 100644 --- a/main/reference/combine_levels.html +++ b/main/reference/combine_levels.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/combine_vectors.html b/main/reference/combine_vectors.html index 488c44e776..c8884af10f 100644 --- a/main/reference/combine_vectors.html +++ b/main/reference/combine_vectors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/compare_variables.html b/main/reference/compare_variables.html index dfbd98eeed..8b4e0865d1 100644 --- a/main/reference/compare_variables.html +++ b/main/reference/compare_variables.html @@ -25,7 +25,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -43,36 +43,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -100,7 +70,7 @@ for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table. -Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic. @@ -249,7 +219,7 @@ ArgumentsValue compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout. s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values. @@ -257,7 +227,7 @@ ValueFunctions compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_compare(): S3 generic function to produce a comparison summary. s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value. diff --git a/main/reference/control_analyze_vars.html b/main/reference/control_analyze_vars.html index 585dd5fd2b..c75272a786 100644 --- a/main/reference/control_analyze_vars.html +++ b/main/reference/control_analyze_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_annot.html b/main/reference/control_annot.html index 15f13721b3..5fd68175f6 100644 --- a/main/reference/control_annot.html +++ b/main/reference/control_annot.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxph.html b/main/reference/control_coxph.html index 208867504f..69af55bbf8 100644 --- a/main/reference/control_coxph.html +++ b/main/reference/control_coxph.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_coxreg.html b/main/reference/control_coxreg.html index e18b38b7b9..db1d7d6769 100644 --- a/main/reference/control_coxreg.html +++ b/main/reference/control_coxreg.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_incidence_rate.html b/main/reference/control_incidence_rate.html index 5e3ab5058b..d19e5835f1 100644 --- a/main/reference/control_incidence_rate.html +++ b/main/reference/control_incidence_rate.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_lineplot_vars.html b/main/reference/control_lineplot_vars.html index 26cb4b47ab..952548ec94 100644 --- a/main/reference/control_lineplot_vars.html +++ b/main/reference/control_lineplot_vars.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_logistic.html b/main/reference/control_logistic.html index d7aa11e3ce..6196aed11d 100644 --- a/main/reference/control_logistic.html +++ b/main/reference/control_logistic.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_riskdiff.html b/main/reference/control_riskdiff.html index f3384e4c78..66e5c0900f 100644 --- a/main/reference/control_riskdiff.html +++ b/main/reference/control_riskdiff.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -111,8 +81,8 @@ Argumentsformat (string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)". +difference statistic. See the 3d string options in formatters::list_valid_format_labels() for possible format +strings. Defaults to "xx.x (xx.x - xx.x)". col_label diff --git a/main/reference/control_step.html b/main/reference/control_step.html index 90e4733e6a..e60ca3ac4e 100644 --- a/main/reference/control_step.html +++ b/main/reference/control_step.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -27,36 +27,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_time.html b/main/reference/control_surv_time.html index 0569d67f7c..3f162fd943 100644 --- a/main/reference/control_surv_time.html +++ b/main/reference/control_surv_time.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/control_surv_timepoint.html b/main/reference/control_surv_timepoint.html index 337efbed91..0f22020284 100644 --- a/main/reference/control_surv_timepoint.html +++ b/main/reference/control_surv_timepoint.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/count_cumulative.html b/main/reference/count_cumulative.html index 28861eeb66..c7f227ef2c 100644 --- a/main/reference/count_cumulative.html +++ b/main/reference/count_cumulative.html @@ -21,7 +21,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -39,36 +39,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -223,17 +193,17 @@ ArgumentsValue count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout. s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction. -a_count_cumulative() returns the corresponding list with formatted rtables::CellValue(). +a_count_cumulative() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% + split_cols_by(var = "ACTARM") %>% + add_colcounts() %>% + add_overall_col(label = "All Patients") %>% summarize_num_patients( var = "USUBJID", .stats = c("unique", "nonunique"), @@ -631,7 +601,7 @@ Statistics Functions= "Overall total number of events" ) ) %>% - split_rows_by( + split_rows_by( "AEBODSYS", child_labels = "visible", nested = FALSE, @@ -647,7 +617,7 @@ Statistics Functions) ) %>% count_occurrences(vars = "AEDECOD") %>% - build_table( + build_table( df = adae, alt_counts_df = adsl ) @@ -718,14 +688,14 @@ Response Tableestimate_proportion()
-basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2)
basic_table() %>% - split_cols_by(var = "ARM") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2)
basic_table() %>% + split_cols_by(var = "ARM") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", table_names = "est_prop" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -733,16 +703,16 @@ Response Table#> 95% CI (Wald, with correction) (78.7, 91.5) (58.8, 75.5) (85.6, 96.2)
To specify which arm in the table should be used as the reference, use the argument ref_group from -split_cols_by(). Below we change the reference arm to “B: +split_cols_by(). Below we change the reference arm to “B: Placebo” and so this arm is displayed as the first column:
ref_group
split_cols_by()
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level arguments to modify the type of confidence interval that is calculated: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables. @@ -147,7 +117,7 @@ Argumentsdo_summarize_row_groups (flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels. @@ -204,14 +174,14 @@ Arguments Value - A layout object suitable for passing to further layouting functions, or to rtables::build_table(). + A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————————————————————— @@ -752,15 +722,15 @@ Response Tableconf_level
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables.
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@ - These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions + These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:analyze_num_patients() analyze_vars() compare_vars() @@ -167,7 +137,7 @@ estimate_proportion() estimate_proportion_diff() summarize_ancova() -summarize_colvars(): even if this function uses rtables::analyze_colvars(), +summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@ See also -analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). diff --git a/main/reference/analyze_variables.html b/main/reference/analyze_variables.html index 535a381f15..ad5691b184 100644 --- a/main/reference/analyze_variables.html +++ b/main/reference/analyze_variables.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -35,36 +35,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -309,7 +279,7 @@ ArgumentsValue analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout. s_summary() returns different statistics depending on the class of x. If x is of class numeric, returns a list with the following named numeric items:n: The length() of x. @@ -347,20 +317,20 @@ Valuecount_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here. -a_summary() returns the corresponding list with formatted rtables::CellValue(). +a_summary() returns the corresponding list with formatted rtables::CellValue(). Details Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. +columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset. Functions analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_summary(): S3 generic function to produces a variable summary. s_summary(numeric): Method for numeric class. s_summary(factor): Method for factor class. @@ -386,7 +356,7 @@ Note Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table(). +created from character variables before passing the dataset to rtables::build_table(). To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE. Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is. diff --git a/main/reference/analyze_vars_in_cols.html b/main/reference/analyze_vars_in_cols.html index 46a2ae9e7a..9812fc6390 100644 --- a/main/reference/analyze_vars_in_cols.html +++ b/main/reference/analyze_vars_in_cols.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table. -This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables.
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", conf_level = 0.9 ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————— @@ -771,15 +741,15 @@ Response Tableestimate_proportion_diff()
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff() layout creating function for this: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue(). Functions count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze(). +arguments and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades. a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -85,9 +55,9 @@ -Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls. @@ -127,7 +97,7 @@ Arguments Value - A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() + A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout. diff --git a/main/reference/add_rowcounts.html b/main/reference/add_rowcounts.html index f15ed9c1ae..3bfabf0033 100644 --- a/main/reference/add_rowcounts.html +++ b/main/reference/add_rowcounts.html @@ -11,7 +11,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -29,36 +29,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -81,8 +51,8 @@ -This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups(). +This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups(). diff --git a/main/reference/aesi_label.html b/main/reference/aesi_label.html index 56ac9223e2..6ba17870e5 100644 --- a/main/reference/aesi_label.html +++ b/main/reference/aesi_label.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/afun_riskdiff.html b/main/reference/afun_riskdiff.html index 7d51585115..340913974b 100644 --- a/main/reference/afun_riskdiff.html +++ b/main/reference/afun_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -33,36 +33,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -87,7 +57,7 @@ In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations. @@ -122,7 +92,7 @@ Argumentslabelstr (string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information. @@ -152,7 +122,7 @@ Arguments.all_col_counts (integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()). +taken from alt_counts_df if specified (see rtables::build_table()). .stats @@ -190,13 +160,13 @@ Arguments Value - A list of formatted rtables::CellValue(). + A list of formatted rtables::CellValue(). See also stat_propdiff_ci() for details on risk difference calculation. -Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout. diff --git a/main/reference/afun_selected_stats.html b/main/reference/afun_selected_stats.html index d332e8ad07..266fab2928 100644 --- a/main/reference/afun_selected_stats.html +++ b/main/reference/afun_selected_stats.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report diff --git a/main/reference/analyze_colvars_functions.html b/main/reference/analyze_colvars_functions.html index 8969d640f4..8c9acf8ee4 100644 --- a/main/reference/analyze_colvars_functions.html +++ b/main/reference/analyze_colvars_functions.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -116,7 +86,7 @@ - These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern + These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables. analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups(). -summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups(). +summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars(). +summarize_row_groups() and rtables::analyze_colvars(). See also -summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). -analyze_functions for functions which are wrappers for rtables::analyze(). +summarize_functions for functions which are wrappers for rtables::summarize_row_groups(). +analyze_functions for functions which are wrappers for rtables::analyze(). diff --git a/main/reference/analyze_functions.html b/main/reference/analyze_functions.html index 0649c84532..bfe1890420 100644 --- a/main/reference/analyze_functions.html +++ b/main/reference/analyze_functions.html @@ -75,7 +75,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -93,36 +93,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -144,7 +114,7 @@
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion_diff( vars = "is_rsp", show_labels = "visible", var_labels = "Unstratified Analysis" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————————————— @@ -791,11 +761,11 @@ Response Tabletest_proportion_diff()
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue().
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff(vars = "is_rsp") %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————————————————— @@ -803,14 +773,14 @@ Response TableTo customize the output, we use the method argument to select a Chi-Squared test with Schouten correction. -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade. a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade(). diff --git a/main/reference/abnormal_by_worst_grade_worsen.html b/main/reference/abnormal_by_worst_grade_worsen.html index 501227c553..77b9b59ca8 100644 --- a/main/reference/abnormal_by_worst_grade_worsen.html +++ b/main/reference/abnormal_by_worst_grade_worsen.html @@ -43,7 +43,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -61,36 +61,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -126,7 +96,7 @@ Any: The total number of patients who have worsened from their baseline grades. Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var. @@ -225,19 +195,19 @@ ArgumentsValue count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout. s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any". a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue(). +formatted rtables::CellValue().
method
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names argument and providing unique names through that: -basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x") Below the only tern function is analyze_vars which replaces the -rtables::analyze function above. +rtables::analyze function above. # Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)")) # Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@ -tern 0.9.5.9026 +tern 0.9.6 -Enhancements +Enhancements Added errorbar_width and linetype parameters to g_lineplot. -Reworking of summarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. Added the .formats argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to specify formats. Added the riskdiff argument to tabulate_rsp_subgroups and tabulate_survival_subgroups to allow users to add a risk difference table column, and function control_riskdiff to specify settings for the risk difference column. Added warning to tabulate_rsp_subgroups when pval statistic is selected but df has not been correctly generated to add p-values to the output table. @@ -87,12 +56,13 @@ Enhancementssummarize_glm_count() documentation and all its associated functions to better describe the results and the functions’ purpose. -Bug Fixes -Fixed a bug in a_surv_time that threw an error when split only has "is_event". -Added defaults for d_count_cumulative parameters as described in the documentation. -Empty levels on g_lineplot x-axis are not shown in either plots. +Bug Fixes +Added defaults for d_count_cumulative parameters as described in the documentation. +Fixed a bug of empty levels on g_lineplot x-axis were not shown in either plots. +Fixed a bug in a_surv_time that threw an error when split only has "is_event". Fixed disappearing line in g_lineplot when using only one group or strata level. Fixed defaults for formats and labels in get_formats_from_stats and get_labels_from_stats. Fixed bug for linear scaling factor (scale parameter) being applied to response but not to rate in h_glm_count while all distributions have logarithmic link function. @@ -101,7 +71,7 @@ Bug FixesFixed issue with wrong wrapping due to different \n and vector behavior that did not cope well with split_string(). -Miscellaneous +Miscellaneous Began deprecation of the confusing functions summary_formats and summary_labels. Enhanced general descriptions of analyze and summarize functions throughout package documentation. Finalized deprecation of the strata and cohort_id arguments to g_lineplot. diff --git a/main/pkgdown.yml b/main/pkgdown.yml index 47b10c0840..aa131abaa6 100644 --- a/main/pkgdown.yml +++ b/main/pkgdown.yml @@ -6,7 +6,7 @@ articles: tables: tables.html tern_formats: tern_formats.html tern: tern.html -last_built: 2024-09-20T02:48Z +last_built: 2024-09-24T05:39Z urls: reference: https://insightsengineering.github.io/tern/reference article: https://insightsengineering.github.io/tern/articles diff --git a/main/reference/abnormal.html b/main/reference/abnormal.html index 3e73c19a98..5e8f35fe89 100644 --- a/main/reference/abnormal.html +++ b/main/reference/abnormal.html @@ -33,7 +33,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -51,36 +51,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -224,16 +194,16 @@ ArgumentsValue count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout. s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients. -a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level. a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal(). diff --git a/main/reference/abnormal_by_baseline.html b/main/reference/abnormal_by_baseline.html index d0c027bcff..96579342ef 100644 --- a/main/reference/abnormal_by_baseline.html +++ b/main/reference/abnormal_by_baseline.html @@ -59,7 +59,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -77,36 +77,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -253,17 +223,17 @@ ArgumentsValue count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout. s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts. -a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_baseline(): Statistics function for a single abnormal level. a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline(). diff --git a/main/reference/abnormal_by_marked.html b/main/reference/abnormal_by_marked.html index f0310c942e..d35e9ceea4 100644 --- a/main/reference/abnormal_by_marked.html +++ b/main/reference/abnormal_by_marked.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Any: The number of patients with either single or replicated marked abnormalities. Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction. @@ -237,17 +207,17 @@ ArgumentsValue count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout. s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results. -a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue(). Functions count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze(). +and additional format arguments. This function is a wrapper for rtables::analyze(). s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities. a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked(). diff --git a/main/reference/abnormal_by_worst_grade.html b/main/reference/abnormal_by_worst_grade.html index 4f6f0ff99d..bd6f9eae9c 100644 --- a/main/reference/abnormal_by_worst_grade.html +++ b/main/reference/abnormal_by_worst_grade.html @@ -47,7 +47,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -65,36 +65,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -132,7 +102,7 @@ Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps. -Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir. @@ -229,17 +199,17 @@ ArgumentsValue count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout. s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results. -a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue(). +a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue().
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% test_proportion_diff( vars = "is_rsp", method = "schouten" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————————————————————————————————————————————————————— @@ -822,9 +792,9 @@ Response Tabletable_names
-basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
basic_table() %>% - split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% - add_colcounts() %>% +basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
basic_table() %>% + split_cols_by(var = "ARM", ref_group = "B: Placebo") %>% + add_colcounts() %>% estimate_proportion( vars = "is_rsp", method = "clopper-pearson", @@ -842,7 +812,7 @@ Response Table method = "schouten", table_names = "test_prop_diff" ) %>% - build_table(anl) + build_table(anl) #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————————————————————————————————————————————— diff --git a/main/articles/tern.html b/main/articles/tern.html index d5e1565344..85e4fd6c7b 100644 --- a/main/articles/tern.html +++ b/main/articles/tern.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -125,7 +95,7 @@ Introduction to ternIt +It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables. @@ -178,10 +148,10 @@ Analytical Functions for rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
It is strongly recommended that you start by reading the “Introduction to rtables” vignette to get familiar with the concept of rtables.
rtables rtables layout functions, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is -materialized with the rtables::build_table function and the +materialized with the rtables::build_table function and the data. The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects. Examples of the tern analytical functions are @@ -193,7 +163,7 @@ Analytical Functions for rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
The tern analytical functions are wrappers around the -rtables::analyze function; they offer various methods +rtables::analyze function; they offer various methods useful from the perspective of clinical trials and other statistical projects.
Examples of the tern analytical functions are @@ -193,7 +163,7 @@
rtables In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@ Analytical Functions for rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
In the rtables code below we first describe the two tables and assign the descriptions to the variables lyt and lyt2. We then built the tables using the actual data with -rtables::build_table. The description of a table is called +rtables::build_table. The description of a table is called a table layout. The analyze instruction adds to the layout that the ARM variable should be analyzed with the mean analysis function @@ -206,25 +176,25 @@
lyt
lyt2
ARM
mean
rtables Defining the table layout with a pure rtables code. # Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x") +lyt <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% + rtables::analyze(vars = "AVAL", mean, format = "xx.x")
Defining the table layout with a pure rtables code.
# Create table layout pure rtables -lyt <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% - rtables::analyze(vars = "AVAL", mean, format = "xx.x")
Below the only tern function is analyze_vars which replaces the -rtables::analyze function above.
analyze_vars
# Create table layout with tern analyze_vars analyze function -lyt2 <- rtables::basic_table() %>% - rtables::split_cols_by(var = "ARM") %>% - rtables::split_rows_by(var = "AVISIT") %>% +lyt2 <- rtables::basic_table() %>% + rtables::split_cols_by(var = "ARM") %>% + rtables::split_rows_by(var = "AVISIT") %>% analyze_vars(vars = "AVAL", .formats = c("mean_sd" = "(xx.xx, xx.xx)"))
# Apply table layout to data and produce `rtables` object adrs <- formatters::ex_adrs -rtables::build_table(lyt, df = adrs) +rtables::build_table(lyt, df = adrs) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> SCREENING @@ -235,7 +205,7 @@ Analytical Functions for rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@
rtables #> mean 1.7 2.1 1.6 #> FOLLOW UP #> mean 2.2 2.9 2.0 -rtables::build_table(lyt2, df = adrs) +rtables::build_table(lyt2, df = adrs) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————— #> SCREENING diff --git a/main/articles/tern_formats.html b/main/articles/tern_formats.html index e42bb63af9..488f37ccf7 100644 --- a/main/articles/tern_formats.html +++ b/main/articles/tern_formats.html @@ -28,7 +28,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -49,36 +49,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report @@ -162,7 +132,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -170,7 +140,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————— #> low 2/2 (100%) @@ -190,7 +160,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -198,7 +168,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = format_fraction) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 1/2 (50%) @@ -219,7 +189,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -227,7 +197,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx / xx") ) %>% - build_table(df2) + build_table(df2) #> all obs #> —————————————— #> low 2 / 2 @@ -248,7 +218,7 @@ Comparing tern & df2 <- df2 %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -256,7 +226,7 @@ Comparing tern & exclude_base_abn = FALSE, .formats = list(fraction = "xx.x / xx.x") ) %>% - build_table(df2) + build_table(df2) #> all obs #> ———————————————— #> low 2.0 / 2.0 @@ -374,7 +344,7 @@ Creating Custom Formatting Functio ) %>% filter(ONTRTFL == "Y") -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -382,7 +352,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = format_fraction_fixed_dp) ) %>% - build_table(df2) + build_table(df2) #> all obs #> ——————————————————— #> low 2/2 (100.0%) @@ -409,7 +379,7 @@ Creating Custom Formatting Functio return(result) } -basic_table() %>% +basic_table() %>% count_abnormal( var = "RANGE", abnormal = list(low = "LOW", high = "HIGH"), @@ -417,7 +387,7 @@ Creating Custom Formatting Functio exclude_base_abn = FALSE, .formats = list(fraction = custom_format) # Here we implement our new custom_format function ) %>% - build_table(df2) + build_table(df2) #> all obs #> ————————————————————— #> low 2/2 (100.000%) diff --git a/main/authors.html b/main/authors.html index 2776f04742..ac3d9df3d5 100644 --- a/main/authors.html +++ b/main/authors.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -136,14 +106,14 @@ Citation Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/. @Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, } diff --git a/main/index.html b/main/index.html index ba9f5b46c0..c15e14adf3 100644 --- a/main/index.html +++ b/main/index.html @@ -31,7 +31,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -52,36 +52,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report diff --git a/main/news/index.html b/main/news/index.html index 5ea338a412..ef2cab22d8 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6 part of @@ -25,36 +25,6 @@ Changelog - - Versions main -latest-tag -release-candidate -v0.9.2-rc1 -v0.9.1-rc3 -v0.8.5-rc2 -v0.8.5-rc1 -v0.8.4-rc1 -v0.8.3-rc2 -v0.8.3-rc1 -v0.8.2-rc2 -v0.8.2-rc1 -v0.7.10 -v0.9.5 -v0.9.4 -v0.9.3 -v0.9.2 -v0.9.1 -v0.9.0 -v0.8.5 -v0.8.4 -v0.8.2 -v0.8.1 -v0.8.0 -v0.7.9 - Reports Coverage report Unit test report @@ -75,11 +45,10 @@
Zhu J, Sabanés Bové D, Stoilova J, Garolini D, de la Rua E, Yogasekaram A, Wang H, Collin F, Waddell A, Rucki P, Liao C, Li J (2024). tern: Create Common TLGs Used in Clinical Trials. -R package version 0.9.5.9026, +R package version 0.9.6, https://github.com/insightsengineering/tern/, https://insightsengineering.github.io/tern/.
@Manual{, title = {tern: Create Common TLGs Used in Clinical Trials}, author = {Joe Zhu and Daniel {Sabanés Bové} and Jana Stoilova and Davide Garolini and Emily {de la Rua} and Abinaya Yogasekaram and Heng Wang and Francois Collin and Adrian Waddell and Pawel Rucki and Chendi Liao and Jennifer Li}, year = {2024}, - note = {R package version 0.9.5.9026, + note = {R package version 0.9.6, https://github.com/insightsengineering/tern/}, url = {https://insightsengineering.github.io/tern/}, }
errorbar_width
linetype
g_lineplot
summarize_glm_count()
.formats
tabulate_rsp_subgroups
tabulate_survival_subgroups
riskdiff
control_riskdiff
pval
a_surv_time
"is_event"
d_count_cumulative
get_formats_from_stats
get_labels_from_stats
scale
h_glm_count
\n
split_string()
summary_formats
summary_labels
strata
cohort_id
count_abnormal() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal() to the table layout.
count_abnormal()
rtables::build_table()
rtable
s_count_abnormal()
s_count_abnormal() returns the statistic fraction which is a vector with num and denom counts of patients.
fraction
num
denom
a_count_abnormal() returns the corresponding list with formatted rtables::CellValue().
a_count_abnormal()
rtables::CellValue()
count_abnormal(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal(): Statistics function which counts patients with abnormal range values for a single abnormal level.
abnormal
a_count_abnormal(): Formatted analysis function which is used as afun in count_abnormal().
count_abnormal_by_baseline() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_baseline() to the table layout.
count_abnormal_by_baseline()
s_count_abnormal_by_baseline()
s_count_abnormal_by_baseline() returns statistic fraction which is a named list with 3 labeled elements: not_abnormal, abnormal, and total. Each element contains a vector with num and denom patient counts.
not_abnormal
total
a_count_abnormal_by_baseline() returns the corresponding list with formatted rtables::CellValue().
a_count_abnormal_by_baseline()
count_abnormal_by_baseline(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal_by_baseline(): Statistics function for a single abnormal level.
a_count_abnormal_by_baseline(): Formatted analysis function which is used as afun in count_abnormal_by_baseline().
Any: The number of patients with either single or replicated marked abnormalities.
Any
Fractions are calculated by dividing the above counts by the number of patients with at least one valid measurement recorded during the analysis.
Prior to using this function in your table layout you must use rtables::split_rows_by() to create two +
rtables::split_rows_by()
Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable direction.
param
direction
count_abnormal_by_marked() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_marked() to the table layout.
count_abnormal_by_marked()
s_count_abnormal_by_marked()
s_count_abnormal_by_marked() returns statistic count_fraction with Single, not last, Last or replicated, and Any results.
count_fraction
Single, not last
Last or replicated
a_count_abnormal_by_marked() returns the corresponding list with formatted rtables::CellValue().
a_count_abnormal_by_marked()
count_abnormal_by_marked(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal_by_marked(): Statistics function for patients with marked lab abnormalities.
a_count_abnormal_by_marked(): Formatted analysis function which is used as afun in count_abnormal_by_marked().
Pre-processing is crucial when using this function and can be done automatically using the h_adlb_abnormal_by_worst_grade() helper function. See the description of this function for details on the necessary pre-processing steps.
h_adlb_abnormal_by_worst_grade()
Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row +
Prior to using this function in your table layout you must use rtables::split_rows_by() to create two row splits, one on variable param and one on variable grade_dir.
grade_dir
count_abnormal_by_worst_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_by_worst_grade() to the table layout.
count_abnormal_by_worst_grade()
s_count_abnormal_by_worst_grade()
s_count_abnormal_by_worst_grade() returns the single statistic count_fraction with grades 1 to 4 and "Any" results.
a_count_abnormal_by_worst_grade() returns the corresponding list with formatted rtables::CellValue().
a_count_abnormal_by_worst_grade()
count_abnormal_by_worst_grade(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal_by_worst_grade(): Statistics function which counts patients by worst grade.
a_count_abnormal_by_worst_grade(): Formatted analysis function which is used as afun in count_abnormal_by_worst_grade().
Any: The total number of patients who have worsened from their baseline grades.
Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades have worsened from baseline toxicity grades during treatment.
Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row +
Prior to using this function in your table layout you must use rtables::split_rows_by() to create a row split on variable direction_var.
direction_var
count_abnormal_lab_worsen_by_baseline() returns a layout object suitable for passing to further layouting -functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted +functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_abnormal_lab_worsen_by_baseline() to the table layout.
count_abnormal_lab_worsen_by_baseline()
s_count_abnormal_lab_worsen_by_baseline()
s_count_abnormal_lab_worsen_by_baseline() returns the counts and fraction of patients whose worst post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades "1", "2", "3", "4" and "Any".
a_count_abnormal_lab_worsen_by_baseline() returns the corresponding list with -formatted rtables::CellValue().
a_count_abnormal_lab_worsen_by_baseline()
count_abnormal_lab_worsen_by_baseline(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_abnormal_lab_worsen_by_baseline(): Statistics function for patients whose worst post-baseline lab grades are worse than their baseline grades.
a_count_abnormal_lab_worsen_by_baseline(): Formatted analysis function which is used as afun diff --git a/main/reference/add_riskdiff.html b/main/reference/add_riskdiff.html index e98e97f31b..03cd65cb55 100644 --- a/main/reference/add_riskdiff.html +++ b/main/reference/add_riskdiff.html @@ -15,7 +15,7 @@ tern - 0.9.5.9026 + 0.9.6
Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference +
rtables::add_combo_levels()
Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function -should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument +should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls.
split_fun
rtables::split_cols_by()
TRUE
A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() +
A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout.
This works analogously to rtables::add_colcounts() but on the rows. This function -is a wrapper for rtables::summarize_row_groups().
rtables::add_colcounts()
rtables::summarize_row_groups()
This works analogously to rtables::add_colcounts() but on the rows. This function +is a wrapper for rtables::summarize_row_groups().
In the risk difference column, this function uses the statistics function associated with afun to calculates risk difference values from arm X (reference group) and arm Y. These arms are specified when configuring the risk difference column which is done using the add_riskdiff() split function in -the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This +the previous call to rtables::split_cols_by(). For all other columns, applies afun as usual. This function utilizes the stat_propdiff_ci() function to perform risk difference calculations.
add_riskdiff()
stat_propdiff_ci()
(string) label of the level of the parent split currently being summarized -(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() +(must be present as second argument in Content Row Functions). See rtables::summarize_row_groups() for more information.
string
(integer) vector where each value represents a global count for a column. Values are -taken from alt_counts_df if specified (see rtables::build_table()).
integer
alt_counts_df
A list of formatted rtables::CellValue().
stat_propdiff_ci() for details on risk difference calculation.
Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with +
Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument set to TRUE in subsequent analyze functions calls, adds a risk difference column to a table layout.
These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern +
rtables::analyze_colvars()
These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.
analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become @@ -129,19 +99,19 @@ it is used for other tables.
analyze_vars_in_cols()
analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis -with rtables::summarize_row_groups().
analyze_patients_exposure_in_cols()
analyze_colvars
summarize_patients_exposure_in_cols()
summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves +with rtables::summarize_row_groups().
summarize_coxreg()
summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to tabulate_* functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of -summarize_row_groups() and rtables::analyze_colvars().
tabulate_*
summarize_functions for functions which are wrappers for rtables::summarize_row_groups().
analyze_functions for functions which are wrappers for rtables::analyze().
These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions +
These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:
analyze_num_patients()
compare_vars()
estimate_proportion()
estimate_proportion_diff()
summarize_ancova()
summarize_colvars(): even if this function uses rtables::analyze_colvars(), +
summarize_colvars()
summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis @@ -182,8 +152,8 @@
analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars().
analyze_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout.
s_summary()
s_summary() returns different statistics depending on the class of x.
x
If x is of class numeric, returns a list with the following named numeric items:
numeric
list
n: The length() of x.
n
length()
count_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here.
NA
a_summary() returns the corresponding list with formatted rtables::CellValue().
a_summary()
Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all -columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset.
vars
"auto"
format_auto()
.df_row[[.var]]
rtables::additional_fun_params
analyze_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_summary(): S3 generic function to produces a variable summary.
s_summary(numeric): Method for numeric class.
s_summary(numeric)
s_summary(factor): Method for factor class.
s_summary(factor)
factor
Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually -created from character variables before passing the dataset to rtables::build_table().
To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE.
compare
Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is.
The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table.
This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). +
This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables.
(flag) defaults to FALSE and applies the analysis to the current -label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr +label rows. This is a wrapper of rtables::summarize_row_groups() and it can accept labelstr to define row labels. This behavior is not supported as we never need to overload row labels.
flag
FALSE
labelstr
A layout object suitable for passing to further layouting functions, or to rtables::build_table(). +
A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.
This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() +
This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases.
In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned @@ -220,7 +190,7 @@
labelstr = NULL
analyze_vars(), rtables::analyze_colvars().
A list with formatted rtables::CellValue() with the row count value and the correct label.
get_stats("analyze_vars_numeric", add_pval = TRUE)
get_stats("analyze_vars_counts", add_pval = TRUE)
.stats
Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column +
Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic.
compare_vars() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout.
s_compare()
s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values.
compare_vars(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_compare(): S3 generic function to produce a comparison summary.
s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value.
s_compare(numeric)
(string or function) the format label (string) or formatting function to apply to the risk -difference statistic. See the 3d string options in list_valid_format_labels() for possible format strings. -Defaults to "xx.x (xx.x - xx.x)".
function
3d
list_valid_format_labels()
"xx.x (xx.x - xx.x)"
formatters::list_valid_format_labels()
count_cumulative() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_cumulative() to the table layout.
count_cumulative()
s_count_cumulative()
s_count_cumulative() returns a named list of count_fractions: a list with each thresholds value as a component, each component containing a vector for the count and fraction.
thresholds
a_count_cumulative() returns the corresponding list with formatted rtables::CellValue().
a_count_cumulative()
count_cumulative(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_cumulative(): Statistics function that produces a named list given a numeric vector of thresholds.
a_count_cumulative(): Formatted analysis function which is used as afun in count_cumulative().
count_missed_doses() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_missed_doses() to the table layout.
count_missed_doses()
s_count_missed_doses()
s_count_nonmissing() returns the statistic n which is the count of non-missing values in x.
s_count_nonmissing()
s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold.
a_count_missed_doses() returns the corresponding list with formatted rtables::CellValue().
a_count_missed_doses()
count_missed_doses(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_nonmissing(): Statistics function to count non-missing values.
s_count_missed_doses(): Statistics function to count patients with missed doses.
a_count_missed_doses(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences.html b/main/reference/count_occurrences.html index 08b078b0c3..c619f35e0d 100644 --- a/main/reference/count_occurrences.html +++ b/main/reference/count_occurrences.html @@ -23,7 +23,7 @@ tern - 0.9.5.9026 + 0.9.6
count_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout.
count_occurrences()
s_count_occurrences()
summarize_occurrences() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout.
summarize_occurrences()
s_count_occurrences() returns a list with:
count: list of counts with one element per occurrence.
count
count_fraction: list of counts and fractions with one element per occurrence.
fraction: list of numerators and denominators with one element per occurrence.
a_count_occurrences() returns the corresponding list with formatted rtables::CellValue().
a_count_occurrences()
count_occurrences(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
summarize_occurrences(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().
s_count_occurrences(): Statistics function which counts number of patients that report an occurrence.
a_count_occurrences(): Formatted analysis function which is used as afun diff --git a/main/reference/count_occurrences_by_grade.html b/main/reference/count_occurrences_by_grade.html index 04f6bdbe9b..1bee422c63 100644 --- a/main/reference/count_occurrences_by_grade.html +++ b/main/reference/count_occurrences_by_grade.html @@ -27,7 +27,7 @@ tern - 0.9.5.9026 + 0.9.6
count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout.
count_occurrences_by_grade()
s_count_occurrences_by_grade()
summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout.
summarize_occurrences_by_grade()
s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping.
a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue().
a_count_occurrences_by_grade()
count_occurrences_by_grade(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze().
summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().
s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade.
a_count_occurrences_by_grade(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_events_in_cols.html b/main/reference/count_patients_events_in_cols.html index c35239095a..153500a45c 100644 --- a/main/reference/count_patients_events_in_cols.html +++ b/main/reference/count_patients_events_in_cols.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6
summarize_patients_events_in_cols() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows +or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_patients_and_multiple_events() to the table layout.
summarize_patients_events_in_cols()
s_count_patients_and_multiple_events()
s_count_patients_and_multiple_events() returns a list with the statistics:
unique: number of unique patients in df.
unique
all: number of rows in df.
all
summarize_patients_events_in_cols(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().
s_count_patients_and_multiple_events(): Statistics function which counts numbers of patients and multiple events defined by filters. Used as analysis function afun in summarize_patients_events_in_cols().
count_patients_with_event() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout.
count_patients_with_event()
s_count_patients_with_event()
s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event.
a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue().
a_count_patients_with_event()
count_patients_with_event(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred.
a_count_patients_with_event(): Formatted analysis function which is used as afun diff --git a/main/reference/count_patients_with_flags.html b/main/reference/count_patients_with_flags.html index 1eeb046974..1145d7c2d1 100644 --- a/main/reference/count_patients_with_flags.html +++ b/main/reference/count_patients_with_flags.html @@ -19,7 +19,7 @@ tern - 0.9.5.9026 + 0.9.6
count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout.
count_patients_with_flags()
s_count_patients_with_flags()
s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag.
n_blq
a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue().
a_count_patients_with_flags()
count_patients_with_flags(): Layout-creating function which can take statistics function -arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE.
a_count_patients_with_flags(): Formatted analysis function which is used as afun diff --git a/main/reference/count_values.html b/main/reference/count_values.html index 09a569e0ae..61909748d4 100644 --- a/main/reference/count_values.html +++ b/main/reference/count_values.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6
count_values() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_values() to the table layout.
count_values()
s_count_values()
s_count_values() returns output of s_summary() for specified values of a non-numeric variable.
a_count_values() returns the corresponding list with formatted rtables::CellValue().
a_count_values()
count_values(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_values(): S3 generic function to count values.
s_count_values(character): Method for character class.
s_count_values(character)
character
s_count_values(factor): Method for factor class. This makes an automatic diff --git a/main/reference/cox_regression.html b/main/reference/cox_regression.html index 384b8a68b9..482fdc3feb 100644 --- a/main/reference/cox_regression.html +++ b/main/reference/cox_regression.html @@ -9,7 +9,7 @@ tern - 0.9.5.9026 + 0.9.6
s_count_values(factor)
summarize_coxreg() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table +or to rtables::build_table(). Adding this function to an rtable layout will add a Cox regression table containing the chosen statistics to the table layout.
s_coxreg() returns the selected statistic for from the Cox regression model for the selected variable(s).
s_coxreg()
a_coxreg() returns formatted rtables::CellValue().
a_coxreg()
s_coxreg(): Statistics function that transforms results tabulated from fit_coxreg_univar() or fit_coxreg_multivar() into a list.
fit_coxreg_univar()
fit_coxreg_multivar()
a_coxreg(): Analysis function which is used as afun in rtables::analyze() -and cfun in rtables::summarize_row_groups() within summarize_coxreg().
cfun
a_coxreg(): Analysis function which is used as afun in rtables::analyze() +and cfun in rtables::summarize_row_groups() within summarize_coxreg().
Automatic conversion of character to factor does not guarantee results can be generated correctly. It is therefore better to always pre-process the dataset such that factors are manually created from character -variables before passing the dataset to rtables::build_table().
These defaults are experimental because we use the names of functions to retrieve the default statistics. This should be generalized in groups of methods according to more reasonable groupings.
Formats in tern and rtables can be functions that take in the table cell value and -return a string. This is well documented in vignette("custom_appearance", package = "rtables").
vignette("custom_appearance", package = "rtables")
estimate_multinomial_response() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_length_proportion() to the table layout.
estimate_multinomial_response()
s_length_proportion()
s_length_proportion() returns statistics from s_proportion().
s_proportion()
a_length_proportion() returns the corresponding list with formatted rtables::CellValue().
a_length_proportion()
estimate_multinomial_response(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze() and -rtables::summarize_row_groups().
s_length_proportion(): Statistics function which feeds the length of x as number of successes, and .N_col as total number of successes and failures into s_proportion().
.N_col
a_length_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/estimate_proportion.html b/main/reference/estimate_proportion.html index e39be87fdf..09cc451f39 100644 --- a/main/reference/estimate_proportion.html +++ b/main/reference/estimate_proportion.html @@ -17,7 +17,7 @@ tern - 0.9.5.9026 + 0.9.6
estimate_proportion() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion() to the table layout.
s_proportion() returns statistics n_prop (n and proportion) and prop_ci (proportion CI) for a given variable.
n_prop
prop_ci
a_proportion() returns the corresponding list with formatted rtables::CellValue().
a_proportion()
estimate_proportion(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_proportion(): Statistics function estimating a proportion along with its confidence interval.
a_proportion(): Formatted analysis function which is used as afun diff --git a/main/reference/ex_data.html b/main/reference/ex_data.html index e4fc796649..d4da47d85b 100644 --- a/main/reference/ex_data.html +++ b/main/reference/ex_data.html @@ -7,7 +7,7 @@ tern - 0.9.5.9026 + 0.9.6
(numeric) variable data the statistics were calculated from. Used only to find significant digits. In analyze_vars this comes from .df_row (see -rtables::additional_fun_params), and it is the row data after the above row splits. No +rtables::additional_fun_params), and it is the row data after the above row splits. No column split is considered.
.df_row
Given a rtables::rtable() object with at least one column with a single value and one column with 2 +
rtables::rtable()
Given a rtables::rtable() object with at least one column with a single value and one column with 2 values, converts table to a ggplot2::ggplot() object and generates an accompanying forest plot. The table and forest plot are printed side-by-side.
ggplot2::ggplot()
This converts a list of group levels into a data frame format which is expected by rtables::add_combo_levels().
(named character or NULL) format patterns for x. Names of the format must -match the names of x. This parameter is passed directly to the rtables::format_rcell +match the names of x. This parameter is passed directly to the rtables::format_rcell function through the format parameter.
NULL
format
rtables::format_rcell
Please see h_tab_surv_one_biomarker() and h_tab_rsp_one_biomarker(), which use this function for examples. -This function is a wrapper for rtables::summarize_row_groups().
h_tab_surv_one_biomarker()
h_tab_rsp_one_biomarker()
estimate_incidence_rate() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_incidence_rate() to the table layout.
estimate_incidence_rate()
s_incidence_rate()
s_incidence_rate() returns the following statistics:
person_years: Total person-years at risk.
person_years
n_events: Total number of events observed.
n_events
n_unique: Total number of patients with at least one event observed.
n_unique
n_rate: Total number of events observed & estimated incidence rate.
n_rate
a_incidence_rate() returns the corresponding list with formatted rtables::CellValue().
a_incidence_rate()
estimate_incidence_rate(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_incidence_rate(): Statistics function which estimates the incidence rate and the associated confidence interval.
a_incidence_rate(): Formatted analysis function which is used as afun in estimate_incidence_rate().
Analyze functions with their corresponding statistics functions and formatted analysis functions.
Analyze functions are used in combination with rtables layout functions in the pipeline which creates the table.
Statistics functions (denoted by s_ prefix) compute the numbers that are tabulated later. In order to separate computation from formatting, they do not take care of rcell type formatting themselves.
s_
Formatted analysis functions (denoted by a_ prefix) have the same arguments as the corresponding statistics functions, and can be further customized by calling rtables::make_afun() on them. They are used as afun in rtables::analyze().
a_
Layout-creating function which creates a multivariate column layout summarizing logistic -regression results. This function is a wrapper for rtables::split_cols_by_multivar().
rtables::split_cols_by_multivar()
Constructor for content functions to be used in summarize_logistic() to summarize -logistic regression results. This function is a wrapper for rtables::summarize_row_groups().
summarize_logistic()
estimate_odds_ratio() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_odds_ratio() to the table layout.
estimate_odds_ratio()
s_odds_ratio()
s_odds_ratio() returns a named list with the statistics or_ci (containing est, lcl, and ucl) and n_tot.
or_ci
est
lcl
ucl
n_tot
a_odds_ratio() returns the corresponding list with formatted rtables::CellValue().
a_odds_ratio()
estimate_odds_ratio(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_odds_ratio(): Statistics function which estimates the odds ratio between a treatment and a control. A variables list with arm and strata variable names must be passed if a stratified analysis is required.
variables
arm
estimate_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_proportion_diff() to the table layout.
s_proportion_diff()
s_proportion_diff() returns a named list of elements diff and diff_ci.
diff
diff_ci
a_proportion_diff() returns the corresponding list with formatted rtables::CellValue().
a_proportion_diff()
estimate_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_proportion_diff(): Statistics function estimating the difference in terms of responder proportion.
a_proportion_diff(): Formatted analysis function which is used as afun in estimate_proportion_diff().
test_proportion_diff() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_test_proportion_diff() to the table layout.
test_proportion_diff()
s_test_proportion_diff()
s_test_proportion_diff() returns a named list with a single item pval with an attribute label describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
label
a_test_proportion_diff() returns the corresponding list with formatted rtables::CellValue().
a_test_proportion_diff()
test_proportion_diff(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_test_proportion_diff(): Statistics function which tests the difference between two proportions.
a_test_proportion_diff(): Formatted analysis function which is used as afun in test_proportion_diff().
keep_rows() returns a pruning function that can be used with rtables::prune_table() +
keep_rows()
rtables::prune_table()
keep_rows() returns a pruning function that can be used with rtables::prune_table() to prune an rtables table.
keep_content_rows() returns a pruning function that checks the condition on the first content row of leaf tables in the table.
keep_content_rows()
Since most table specifications are worded positively, we name our constructor and condition functions positively, too. However, note that the result of keep_rows() says what -should be pruned, to conform with the rtables::prune_table() interface.
An rtables table summarizing binary response by subgroup.
a_response_subgroups() returns the corresponding list with formatted rtables::CellValue().
a_response_subgroups()
tabulate_rsp_subgroups(): Table-creating function which creates a table -summarizing binary response by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups().
tabulate_rsp_subgroups()
a_response_subgroups(): Formatted analysis function which is used as afun in tabulate_rsp_subgroups().
Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using +
Given a rtables::rtable() object, performs basic conversion to a ggplot2::ggplot() object built using functions from the ggplot2 package. Any table titles and/or footnotes are ignored.
ggplot2
rtables::cont_n_allcols()
rtables::cont_n_onecol()
additional arguments to rtables::split_cols_by() in order. For instance, to +
additional arguments to rtables::split_cols_by() in order. For instance, to control formats (format), add a joint column for all groups (incl_all).
incl_all
Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() +
Split function add_riskdiff() which, when used as split_fun within rtables::split_cols_by() with riskdiff argument is set to TRUE in subsequent analyze functions, adds a column containing proportion (risk) difference to an rtables layout.
summarize_ancova() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_ancova() to the table layout.
s_ancova()
s_ancova() returns a named list of 5 statistics:
n: Count of complete sample size for the group.
lsmean: Estimated marginal means in the group.
lsmean
pval: p-value (not adjusted for multiple comparisons).
a_ancova() returns the corresponding list with formatted rtables::CellValue().
a_ancova()
summarize_ancova(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_ancova(): Statistics function that produces a named list of results of the investigated linear model.
a_ancova(): Formatted analysis function which is used as afun in summarize_ancova().
summarize_change() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_change_from_baseline() to the table layout.
summarize_change()
s_change_from_baseline()
s_change_from_baseline() returns the same values returned by s_summary.numeric().
s_summary.numeric()
a_change_from_baseline() returns the corresponding list with formatted rtables::CellValue().
a_change_from_baseline()
summarize_change(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_change_from_baseline(): Statistics function that summarizes baseline or post-baseline visits.
a_change_from_baseline(): Formatted analysis function which is used as afun in summarize_change().
The analyze function summarize_colvars() uses the statistics function s_summary() to analyze variables that are arranged in columns. The variables to analyze should be specified in the table layout via column splits (see -split_cols_by() and split_cols_by_multivar()) prior to using summarize_colvars().
split_cols_by_multivar()
The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different +rtables::split_cols_by() and rtables::split_cols_by_multivar()) prior to using summarize_colvars().
The function is a minimal wrapper for rtables::analyze_colvars(), a function typically used to apply different analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to the analyze_vars_in_cols() function.
rtables::split_cols_by_multivar() and analyze_colvars_functions.
analyze_colvars_functions
These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions +
These functions are wrappers for rtables::summarize_row_groups(), applying corresponding tern content functions to add summary rows to a given table layout:
add_rowcounts()
estimate_multinomial_response() (with rtables::analyze())
h_tab_one_biomarker() (probably to deprecate)
h_tab_one_biomarker()
logistic_summary_by_flag()
Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() -(in combination with several other rtables functions like rtables::analyze_colvars()) to +
Additionally, the summarize_coxreg() function utilizes rtables::summarize_row_groups() +(in combination with several other rtables functions like rtables::analyze_colvars()) to output a Cox regression summary table.
summarize_glm_count() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_glm_count() to the table layout.
s_glm_count()
s_glm_count() returns a named list of 5 statistics:
rate: Estimated event rate per follow-up time.
rate
summarize_glm_count(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_glm_count(): Statistics function that produces a named list of results of the investigated Poisson model.
A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add a logistic regression variable summary to the table layout.
analyze_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout.
s_num_patients_content()
summarize_num_patients() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_num_patients_content() to the table layout.
s_num_patients() returns a named list of 3 statistics:
s_num_patients()
unique: Vector of counts and percentages.
nonunique: Vector of counts.
nonunique
In general, functions that starts with analyze* are expected to -work like rtables::analyze(), while functions that starts with summarize* -are based upon rtables::summarize_row_groups(). The latter provides a +work like rtables::analyze(), while functions that starts with summarize* +are based upon rtables::summarize_row_groups(). The latter provides a value for each dividing split in the row and column space, but, being it bound to the fundamental splits, it is repeated by design in every page when pagination is involved.
analyze*
summarize*
analyze_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
summarize_num_patients(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().
s_num_patients(): Statistics function which counts the number of unique patients, the corresponding percentage taken with respect to the total number of patients, and the number of non-unique patients.
analyze_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted data rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout.
s_count_patients_sum_exposure()
summarize_patients_exposure_in_cols() returns a layout object suitable for passing to further -layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will +layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows, with the statistics from s_count_patients_sum_exposure() arranged in columns, to the table layout.
s_count_patients_sum_exposure() returns a named list with the statistics:
n_patients: Number of unique patients in df.
n_patients
sum_exposure: Sum of ex_var across all patients in df.
sum_exposure
ex_var
a_count_patients_sum_exposure() returns formatted rtables::CellValue().
a_count_patients_sum_exposure()
analyze_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::analyze_colvars().
summarize_patients_exposure_in_cols(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for -rtables::split_cols_by_multivar() and rtables::summarize_row_groups().
s_count_patients_sum_exposure(): Statistics function which counts numbers of patients and the sum of exposure across all patients.
a_count_patients_sum_exposure(): Analysis function which is used as afun in -rtables::analyze_colvars() within analyze_patients_exposure_in_cols() and as cfun in -rtables::summarize_row_groups() within summarize_patients_exposure_in_cols().
coxph_pairwise() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_coxph_pairwise() to the table layout.
coxph_pairwise()
s_coxph_pairwise()
s_coxph_pairwise() returns the statistics:
pvalue: p-value to test the null hypothesis that hazard ratio = 1.
pvalue
hr: Hazard ratio.
hr
n_tot: Total number of observations.
n_tot_events: Total number of events.
n_tot_events
a_coxph_pairwise() returns the corresponding list with formatted rtables::CellValue().
a_coxph_pairwise()
coxph_pairwise(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_coxph_pairwise(): Statistics function which analyzes HR, CIs of HR, and p-value of a Cox-PH model.
a_coxph_pairwise(): Formatted analysis function which is used as afun in coxph_pairwise().
An rtables table summarizing survival by subgroup.
a_survival_subgroups() returns the corresponding list with formatted rtables::CellValue().
a_survival_subgroups()
tabulate_survival_subgroups(): Table-creating function which creates a table -summarizing survival by subgroup. This function is a wrapper for rtables::analyze_colvars() -and rtables::summarize_row_groups().
tabulate_survival_subgroups()
a_survival_subgroups(): Formatted analysis function which is used as afun in tabulate_survival_subgroups().
surv_time() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_time() to the table layout.
surv_time()
s_surv_time()
s_surv_time() returns the statistics:
median: Median survival time.
median
median_ci: Confidence interval for median time.
median_ci
range_event: Survival time range for observations with events.
range_event
range: Survival time range for all observations.
range
a_surv_time() returns the corresponding list with formatted rtables::CellValue().
a_surv_time()
surv_time(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_surv_time(): Statistics function which analyzes survival times.
a_surv_time(): Formatted analysis function which is used as afun in surv_time().
surv_timepoint() returns a layout object suitable for passing to further layouting functions, -or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing +or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_surv_timepoint() and/or s_surv_timepoint_diff() to the table layout depending on the value of method.
surv_timepoint()
s_surv_timepoint()
s_surv_timepoint_diff()
s_surv_timepoint() returns the statistics:
pt_at_risk: Patients remaining at risk.
pt_at_risk
rate_se: Standard error of event free rate.
rate_se
rate_ci: Confidence interval for event free rate.
rate_ci
a_surv_timepoint() returns the corresponding list with formatted rtables::CellValue().
a_surv_timepoint()
s_surv_timepoint_diff() returns the statistics:
rate_diff: Event-free rate difference between two groups.
rate_diff
rate_diff_ci: Confidence interval for the difference.
rate_diff_ci
ztest_pval: p-value to test the difference is 0.
ztest_pval
a_surv_timepoint_diff() returns the corresponding list with formatted rtables::CellValue().
a_surv_timepoint_diff()
surv_timepoint(): Layout-creating function which can take statistics function arguments -and additional format arguments. This function is a wrapper for rtables::analyze().
s_surv_timepoint(): Statistics function which analyzes survival rate.
a_surv_timepoint(): Formatted analysis function which is used as afun in surv_timepoint() when method = "surv".
method = "surv"
Collection of useful functions that are expanding on the core list of functions -provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() +provided by rtables. See rtables::custom_split_funs and rtables::make_split_fun() for more information on how to make a custom split function. All these functions -work with rtables::split_rows_by() argument split_fun to modify the way the split -happens. For other split functions, consider consulting rtables::split_funcs.
rtables::make_split_fun()
rtables::split_funcs
