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<h1 class="title toc-ignore">PD, Dose-Response - Count</h1>
<h4 class="author">Alison Margolskee</h4>
</div>
<div id="overview" class="section level2">
<h2>Overview</h2>
<!--START_EXPLANATION-->
<p>This document contains exploratory plots for count PD data as well as
the R code that generates these graphs. The plots presented here are
based on simulated data (<a href="PKPD_Datasets.html">see: PKPD
Datasets</a>). Data specifications can be accessed on <a
href="Datasets.html">Datasets</a> and Rmarkdown template to generate
this page can be found on <a
href="Rmarkdown/Multiple_Ascending_Dose_PD_count.Rmd">Rmarkdown-Template</a>.
You may also download the Multiple Ascending Dose PK/PD dataset for your
reference (<a href="Data/Multiple_Ascending_Dose_Dataset2.csv">download
dataset</a>). <!--END_EXPLANATION--></p>
</div>
<div id="setup" class="section level2">
<h2>Setup</h2>
<pre class="r"><code>library(ggplot2)
library(dplyr)
library(tidyr)
library(xgxr)
#flag for labeling figures as draft
status = "DRAFT"
## ggplot settings
xgx_theme_set()
#directories for saving individual graphs
dirs = list(
parent_dir = "Parent_Directory",
rscript_dir = "./",
rscript_name = "Example.R",
results_dir = "./",
filename_prefix = "",
filename = "Example.png")</code></pre>
</div>
<div id="load-dataset" class="section level2">
<h2>Load Dataset</h2>
<pre class="r"><code>#load dataset
pkpd_data <- read.csv("../Data/Multiple_Ascending_Dose_Dataset2.csv")
DOSE_CMT = 1
PD_CMT = 4
SS_PROFDAY = 6 # steady state prof day
PD_PROFDAYS = c(0, 2, 4, 6)
#ensure dataset has all the necessary columns
pkpd_data = pkpd_data %>%
mutate(ID = ID, #ID column
TIME = TIME, #TIME column name
NOMTIME = NOMTIME, #NOMINAL TIME column name
PROFDAY = case_when(
NOMTIME < (SS_PROFDAY - 1)*24 ~ 1 + floor(NOMTIME / 24),
NOMTIME >= (SS_PROFDAY - 1)*24 ~ SS_PROFDAY
), #PROFILE DAY day associated with profile, e.g. day of dose administration
LIDV = LIDV, #DEPENDENT VARIABLE column name
CENS = CENS, #CENSORING column name
CMT = CMT, #COMPARTMENT column
DOSE = DOSE, #DOSE column here (numeric value)
TRTACT = TRTACT, #DOSE REGIMEN column here (character, with units),
LIDV_UNIT = EVENTU,
DAY_label = ifelse(PROFDAY > 0, paste("Day", PROFDAY), "Baseline")
)
#create a factor for the treatment variable for plotting
pkpd_data = pkpd_data %>%
arrange(DOSE) %>%
mutate(TRTACT_low2high = factor(TRTACT, levels = unique(TRTACT)),
TRTACT_high2low = factor(TRTACT, levels = rev(unique(TRTACT)))) %>%
select(-TRTACT)
#create pd dataset
pd_data <- pkpd_data %>%
filter(CMT == PD_CMT) %>%
mutate(count_low2high = factor(LIDV, levels = sort(unique(LIDV))),
count_high2low = factor(LIDV, levels = rev(sort(unique(LIDV)))))
#units and labels
time_units_dataset = "hours"
time_units_plot = "days"
trtact_label = "Dose"
dose_units = unique((pkpd_data %>% filter(CMT == DOSE_CMT))$LIDV_UNIT) %>% as.character()
dose_label = paste0("Dose (", dose_units, ")")
pd_units = unique(pd_data$LIDV_UNIT) %>% as.character()
pd_label = paste0("PD Marker (", pd_units, ")") </code></pre>
</div>
<div id="provide-an-overview-of-the-data" class="section level2">
<h2>Provide an overview of the data</h2>
<!--START_EXPLANATION-->
<p>Summarize the data in a way that is easy to visualize the general
trend of PD over time and between doses. Using summary statistics can be
helpful, e.g. Mean +/- SE, or median, 5th & 95th percentiles.
<!--END_EXPLANATION--></p>
<div
id="count-over-time-colored-by-dose-median-5th-95th-percentiles-by-nominal-time"
class="section level3">
<h3>Count over time, colored by Dose, median, 5th & 95th percentiles
by nominal time</h3>
<pre class="r"><code>gg <- ggplot(data = pd_data,
aes(x = NOMTIME, y = LIDV, color = TRTACT_high2low, fill = TRTACT_high2low))
gg <- gg + xgx_geom_pi(percent_level = 0.95, geom = c("line", "point", "errorbar"), alpha = 0.5, position = position_dodge(-9.6))
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + ylab(pd_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-3-1.png" width="768" /></p>
</div>
<div
id="count-over-time-faceted-by-dose-median-5th-95th-percentiles-by-nominal-time"
class="section level3">
<h3>Count over time, faceted by Dose, median, 5th & 95th percentiles
by nominal time</h3>
<pre class="r"><code>gg <- ggplot(data = pd_data,
aes(x = NOMTIME, y = LIDV))
gg <- gg + xgx_geom_pi(percent_level = 0.95, geom = c("line", "point", "errorbar"))
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + facet_grid(~TRTACT_low2high)
gg <- gg + ylab(pd_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-4-1.png" width="960" /></p>
</div>
<div id="percent-of-subjects-by-count-over-time-faceted-by-dose"
class="section level3">
<h3>Percent of subjects by Count over time, faceted by dose</h3>
<pre class="r"><code>gg <- ggplot(data = pd_data, aes(x = factor(PROFDAY), fill = count_high2low))
gg <- gg + geom_bar(position = "fill") + scale_fill_grey(start = 0.8, end = 0.2, breaks = rev(seq(0, 18, 6)))
gg <- gg + scale_y_continuous(labels = scales::percent)
gg <- gg + ylab("Percent of Subjects") + xlab("Time (days)") + guides(fill = guide_legend(pd_label))
gg <- gg + facet_grid(.~TRTACT_low2high)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-5-1.png" width="960" /></p>
</div>
</div>
<div id="explore-variability" class="section level2">
<h2>Explore variability</h2>
<!--START_EXPLANATION-->
<p>Use spaghetti plots to visualize the extent of variability between
individuals. The wider the spread of the profiles, the higher the
between subject variability. Distinguish different doses by color, or
separate into different panels. If coloring by dose, do the individuals
in the different dose groups overlap across doses? Does there seem to be
more variability at higher or lower concentrations?
<!--END_EXPLANATION--></p>
<div id="spaghetti-plots-of-count-over-time-faceted-by-dose"
class="section level3">
<h3>Spaghetti plots of Count over time, faceted by dose</h3>
<pre class="r"><code>gg <- ggplot(data = pd_data,
aes(x = NOMTIME, y = LIDV))
gg <- gg + geom_point(size = 2, alpha = 0.5)
gg <- gg + geom_line(aes(group = ID), alpha = 0.5)
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + ylab(pd_label)
gg <- gg + facet_grid(~TRTACT_low2high)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-6-1.png" width="960" /></p>
</div>
</div>
<div id="explore-irregularities-in-profiles" class="section level2">
<h2>Explore irregularities in profiles</h2>
<!--START_EXPLANATION-->
<p>Plot individual profiles in order to inspect them for any
irregularities. Inspect the profiles for outlying data points that may
skew results or bias conclusions. <!--END_EXPLANATION--></p>
<div id="count-over-time-faceted-by-individual-individual-line-plots"
class="section level3">
<h3>Count over time, faceted by individual, individual line plots</h3>
<pre class="r"><code>ncol = length(unique(pd_data$ID))/length(unique(pd_data$DOSE))
gg <- ggplot(data = pd_data, aes(x = NOMTIME, y = LIDV))
gg <- gg + geom_point(size = 2)
gg <- gg + geom_line(aes(group = ID))
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + facet_wrap(~ID + TRTACT_low2high, ncol = ncol)
gg <- gg + theme(panel.grid.minor.x = ggplot2::element_line(color = rgb(0.9, 0.9, 0.9)),
panel.grid.minor.y = ggplot2::element_line(color = rgb(0.9, 0.9, 0.9)))
gg <- gg + xgx_annotate_status(status, fontsize = 4, color = rgb(0.5, 0.5, 1))
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-7-1.png" width="960" /></p>
</div>
</div>
<div id="explore-covariate-effects-on-pd" class="section level2">
<h2>Explore covariate effects on PD</h2>
<!--START_EXPLANATION-->
<p>Stratify by covariates of interest to explore whether any key
covariates impact response. For examples of plots and code startifying
by covariate, see <a
href="Single_Ascending_Dose_PK.html#explore_covariate_effects_on_pk">Single
Ascending Dose Covariate Section</a></p>
<p><strong>Warning</strong> Be careful of interpreting covariate effects
on PD. Covariate effects on PD could be the result of covariate effects
on PK transfering to PD through the PK/PD relationship.
<!--END_EXPLANATION--></p>
</div>
<div id="explore-dose-response-relationship" class="section level2">
<h2>Explore Dose-Response Relationship</h2>
<div
id="count-vs-dose-colored-by-time-median-5th-95th-percentiles-by-nominal-time"
class="section level3">
<h3>Count vs Dose, colored by time, median, 5th & 95th percentiles
by nominal time</h3>
<pre class="r"><code>data_to_plot <- pd_data %>% subset(PROFDAY %in% PD_PROFDAYS)
gg <- ggplot(data = data_to_plot,
aes(x = DOSE, y = LIDV, color = DAY_label, fill = DAY_label))
gg <- gg + xgx_geom_pi(percent_level = 0.95,
geom = c("point", "errorbar"), position = position_dodge(50))
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + scale_x_continuous(breaks = unique(data_to_plot$DOSE))
gg <- gg + xlab(dose_label) + ylab(pd_label)
gg <- gg + geom_smooth(method = "glm", method.args = list(family = poisson), position = position_dodge(50))
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-8-1.png" width="768" /></p>
</div>
<div id="count-vs-dose-faceted-by-time-boxplots-by-nominal-time"
class="section level3">
<h3>Count vs Dose, faceted by Time, boxplots by nominal time</h3>
<pre class="r"><code>data_to_plot <- pd_data %>% subset(PROFDAY %in% SS_PROFDAY)
gg <- ggplot(data = data_to_plot,
aes(x = DOSE, y = LIDV))
gg <- gg + geom_boxplot(aes(group = DOSE))
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + geom_smooth(method = "glm", method.args = list(family = poisson), color = "black")
gg <- gg + xlab(dose_label)
gg <- gg + ylab(pd_label)
gg <- gg + facet_grid(~DAY_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-9-1.png" width="480" /></p>
</div>
<div id="count-vs-dose-faceted-by-time-boxplots-by-nominal-time-1"
class="section level3">
<h3>Count vs Dose, faceted by Time, boxplots by nominal time</h3>
<pre class="r"><code>gg %+% (data = pd_data %>% subset(PROFDAY %in% PD_PROFDAYS))</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-10-1.png" width="960" /></p>
</div>
<div id="percent-of-subjects-by-count" class="section level3">
<h3>Percent of subjects by Count</h3>
<pre class="r"><code>data_to_plot <- pd_data %>% subset(PROFDAY %in% SS_PROFDAY)
gg <- ggplot(data = data_to_plot, aes(x = factor(DOSE), fill = count_high2low))
gg <- gg + geom_bar(position = "fill") + scale_fill_grey(start = 0.8, end = 0.2, breaks = rev(seq(0, 18, 6)))
gg <- gg + scale_y_continuous(labels = scales::percent)
gg <- gg + ylab("Percent of Subjects") + xlab(dose_label)
gg <- gg + guides(fill = guide_legend(""))
gg <- gg + facet_grid(.~DAY_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-11-1.png" width="480" /></p>
</div>
<div id="percent-of-subjects-by-count-faceted-by-time"
class="section level3">
<h3>Percent of subjects by Count, faceted by time</h3>
<pre class="r"><code>gg %+% (data = pd_data %>% subset(PROFDAY %in% PD_PROFDAYS))</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_count_files/figure-html/unnamed-chunk-12-1.png" width="960" /></p>
</div>
</div>
<div id="r-session-info" class="section level2">
<h2>R Session Info</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server 7.9 (Maipo)
##
## Matrix products: default
## BLAS/LAPACK: /CHBS/apps/EB/software/imkl/2019.1.144-gompi-2019a/compilers_and_libraries_2019.1.144/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
## [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] DT_0.26 forcats_0.5.2 stringr_1.4.1 purrr_0.3.5 readr_2.1.3 tibble_3.1.8 tidyverse_1.3.2 xgxr_1.1.1 zoo_1.8-11
## [10] gridExtra_2.3 tidyr_1.2.1 dplyr_1.0.10 ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] googledrive_2.0.0 colorspace_2.0-3 deldir_1.0-6 ellipsis_0.3.2 class_7.3-19 htmlTable_2.2.1 markdown_1.2
## [8] base64enc_0.1-3 fs_1.5.2 gld_2.6.2 rstudioapi_0.14 proxy_0.4-26 farver_2.1.1 Deriv_4.1.3
## [15] fansi_1.0.3 mvtnorm_1.1-3 lubridate_1.8.0 xml2_1.3.3 codetools_0.2-18 splines_4.1.0 cachem_1.0.6
## [22] rootSolve_1.8.2.2 knitr_1.40 Formula_1.2-4 jsonlite_1.8.3 broom_1.0.1 binom_1.1-1 cluster_2.1.3
## [29] dbplyr_2.2.1 png_0.1-7 compiler_4.1.0 httr_1.4.4 backports_1.4.1 assertthat_0.2.1 Matrix_1.5-1
## [36] fastmap_1.1.0 gargle_1.2.1 cli_3.4.1 prettyunits_1.1.1 htmltools_0.5.3 tools_4.1.0 gtable_0.3.1
## [43] glue_1.6.2 lmom_2.8 Rcpp_1.0.9 cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.5.0 nlme_3.1-160
## [50] crosstalk_1.2.0 xfun_0.34 rvest_1.0.3 lifecycle_1.0.3 googlesheets4_1.0.1 MASS_7.3-58.1 scales_1.2.1
## [57] hms_1.1.2 expm_0.999-6 RColorBrewer_1.1-3 yaml_2.3.6 Exact_2.1 pander_0.6.4 sass_0.4.2
## [64] rpart_4.1.16 reshape_0.8.8 latticeExtra_0.6-30 stringi_1.7.8 highr_0.9 e1071_1.7-8 checkmate_2.1.0
## [71] boot_1.3-28 rlang_1.0.6 pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.17 lattice_0.20-45 htmlwidgets_1.5.4
## [78] labeling_0.4.2 tidyselect_1.2.0 GGally_2.1.2 plyr_1.8.7 magrittr_2.0.3 R6_2.5.1 DescTools_0.99.42
## [85] generics_0.1.3 Hmisc_4.7-0 DBI_1.1.3 pillar_1.8.1 haven_2.5.1 foreign_0.8-82 withr_2.5.0
## [92] mgcv_1.8-41 survival_3.4-0 RCurl_1.98-1.4 nnet_7.3-17 crayon_1.5.2 modelr_0.1.9 interp_1.1-2
## [99] utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.17 progress_1.2.2 jpeg_0.1-9 grid_4.1.0 readxl_1.4.1
## [106] minpack.lm_1.2-1 data.table_1.14.2 reprex_2.0.2 digest_0.6.30 munsell_0.5.0 bslib_0.4.0</code></pre>
</div>
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