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<h1 class="title toc-ignore">PD - Multiple Ascending Dose - Realistic
Data</h1>
<h4 class="author">Alison Margolskee, Kostas Biliouris</h4>
</div>
<div id="overview" class="section level2">
<h2>Overview</h2>
<!--START_EXPLANATION-->
<p>This document contains PD exploratory graphs and also the R code that
generates these graphs. The plots presented here are inspired by a real
study which involved multiple types of PD data, from continuous
endpoints to ordinal response and count data.</p>
<p>This code makes use of the following files which can be downloaded
from the available links</p>
<ul>
<li><a href="Data/mt12345.csv">blinded dataset of PK and PD
endpoints</a></li>
</ul>
<p>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_real_example.Rmd">Rmarkdown-Template</a>.
<!--END_EXPLANATION--></p>
</div>
<div id="setup" class="section level2">
<h2>Setup</h2>
<pre class="r"><code># library(rmarkdown)
library(ggplot2)
library(dplyr)
library(caTools)
library(xgxr)
#flag for labeling figures as draft
status = "DRAFT"
## ggplot settings
xgx_theme_set()</code></pre>
</div>
<div id="loading-dataset" class="section level2">
<h2>Loading dataset</h2>
<pre class="r"><code>pkpd_data <- read.csv(file = "../Data/mt12345.csv", header = T)
#ensure dataset has all the necessary columns
pkpd_data = pkpd_data %>%
mutate(ID = ID, #ID column
TIME = TIM2, #TIME column name, time from first dose administration
NOMTIME = NTIM, #NOMINAL TIME column name
PROFDAY = 1 + floor(NOMTIME / 24), #PROFILE DAY day associated with profile, e.g. day of dose administration
PROFTIME = NOMTIME - (PROFDAY - 1)*24, #PROFILE TIME, time associated with profile, e.g. hours post dose
EVID = EVID , #EVENT ID, > = 1 is dose, otherwise measurement
LIDV = LIDV, #DEPENDENT VARIABLE column name
CENS = CENS, #CENSORING column name
CMT = CMT, #COMPARTMENT column
DOSE = RNDDOSE, #DOSE column here (numeric value)
TRTACT = TRTTXT, #DOSE REGIMEN column here (character, with units),
LIDV_NORM = LIDV/DOSE,
LIDV_UNIT = UNIT,
DAY_label = paste("Day", PROFDAY),
NOMTIME_label = paste(NOMTIME, "h"),
PROFTIME_label = paste(PROFTIME, "h")
)
#create a factor for the treatment variable for plotting
pkpd_data = pkpd_data %>%
arrange(DOSE) %>%
mutate(TRTACT_low2high = factor(paste0(DOSE, " mg"), levels = unique(paste0(DOSE, " mg"))),
TRTACT_high2low = factor(paste0(DOSE, " mg"), levels = rev(unique(paste0(DOSE, " mg")))))
# data cleaning
pkpd_data <- pkpd_data %>%
subset(ID < 1109 | ID > 1117,) %>% #Remove data from Part 3 and placebo
# subset(NOMTIME> = 0,) %>% # Remove negative times
unique() %>%
group_by(ID) %>%
mutate(MAD = ifelse(sum(EVID) > 1, 1, 0)) %>% # Identify multiple dose subjects
ungroup()
pk_data <- pkpd_data %>% subset(CMT == 2)
pd_data <- pkpd_data %>%
subset(CMT == 13) %>%
subset(CENS == 0) %>%
group_by(ID) %>%
mutate(CHG = LIDV - LIDV[NOMTIME == 0]) ##create column with change from baseline
# Separate the SAD and MAD datasets for plotting
pd_single_dosing <- pd_data %>%
subset(MAD == 0)
pd_multiple_dosing <- pd_data %>%
subset(MAD == 1 & DOSE != 100) ##remove dose = 100mg as there is only zero time for that.
#units and labels
time_units_dataset = "hours"
time_units_plot = "days"
trtact_label = "Dose"
dose_units = unique((pkpd_data %>% filter(CMT == 1) )$LIDV_UNIT) %>% as.character()
dose_label = paste0("Dose (", dose_units, ")")
conc_units = unique(pk_data$LIDV_UNIT) %>% as.character()
conc_label = paste0("Concentration (", conc_units, ")")
concnorm_label = paste0("Normalized Concentration (", conc_units, ")/", dose_units)
AUC_units = paste0("h.", conc_units)
pd_label = "Intensity Score"
#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="intensity-score-a-composite-score" class="section level2">
<h2>Intensity Score (a Composite Score)</h2>
<!--START_EXPLANATION-->
<p>Intensity Score is a composite score ranging from 0 to 28, coming
from the sum of 7 categories each with possible values from 0 to 4. The
hypothesis for drug ABC123 is that it will have a positive relationship
with Intensity Score. If the drug is working, higher doses should result
in higher Intensity Score. <!--END_EXPLANATION--></p>
<div id="response-over-time" class="section level3">
<h3>Response over Time</h3>
<div id="intensity-score-over-time-faceting-by-dose"
class="section level4">
<h4>Intensity Score over Time, Faceting by Dose</h4>
<!--START_EXPLANATION-->
<p>Lets get an overview of the change from baseline intensity score over
time following multiple dosing. Plotting boxplots of the Change from
Baseline Intensity score over time, grouped by different treatments, we
can begin to see the behavior of the drug over time and by treatment.
Looking at the Placebo and 30 mg dose groups, the change from baseline
intensity score does not appear to be different from zero on days 7 or
14. However, with the 30 mg and 50 mg treatment groups, change from
baseline intensity score is clearly greater than zero for days 7 and 14.
Drug ABC123 appears to start working for 30 mg doses and higher.
<!--END_EXPLANATION--></p>
<pre class="r"><code>gg <- ggplot(pd_multiple_dosing,
aes(y = CHG, x = PROFDAY, group = NOMTIME))
gg <- gg + geom_boxplot(width = 5)
gg <- gg + ggtitle("Multiple dosing")
gg <- gg + geom_hline(yintercept = 0,
color = "red", linetype = "dashed")
gg <- gg + scale_x_continuous(breaks = c(-1,7,14))
gg <- gg + theme(axis.text = element_text(size = 13),
axis.title = element_text(size = 14),
strip.text = element_text(size = 14))
gg <- gg + ylab(paste0(pd_label,"\nchange from baseline"))
gg <- gg + xlab("Time (days)")
gg <- gg + facet_wrap(~TRTACT_low2high, nrow = 1)
gg <- gg + xgx_annotate_status()
gg <- gg + xgx_annotate_filenames(dirs)
gg</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_real_example_files/figure-html/unnamed-chunk-3-1.png" width="960" /></p>
</div>
</div>
<div id="dose-response" class="section level3">
<h3>Dose Response</h3>
<div id="intensity-score-vs-dose" class="section level4">
<h4>Intensity Score vs Dose</h4>
<!--START_EXPLANATION-->
<p>For this fast acting drug, an effect in change from baseline
intensity score can actually be seen within the first 24 hours. In order
to get an idea of the dose response relationship and make sure we are
targeting an optimal dose, take a look at the response for a range of
doses that were studied in the single ascending dose study. Plotting
boxplots of the change from baseline intensity score against dose, you
can see that starting at 10 mg, drug ABC123 has a clear effect on
intensity score. <!--END_EXPLANATION--></p>
<pre class="r"><code>gg <- ggplot(pd_single_dosing %>% subset(NOMTIME == 24),
aes(y = CHG, x = factor(DOSE)))
gg <- gg + geom_boxplot(aes(group = factor(DOSE)))
gg <- gg + ggtitle("Single dosing")
gg <- gg + geom_hline(yintercept = 0,
color = "red",linetype = "dashed")
gg <- gg + theme(axis.text = element_text(size = 13),
axis.title = element_text(size = 14),
strip.text = element_text(size = 14))
gg <- gg + ylab(paste0(pd_label,"\nchange from baseline"))
gg <- gg + xlab(dose_label)
gg <- gg + facet_wrap(~NOMTIME_label, nrow = 1)
gg <- gg + xgx_annotate_status()
gg <- gg + xgx_annotate_filenames(dirs)
gg</code></pre>
<p><img src="Multiple_Ascending_Dose_PD_real_example_files/figure-html/unnamed-chunk-4-1.png" width="960" /></p>
<!--START_EXPLANATION-->
<p>In the plot above, the doses are uniformly spaced, not proportionally
spaced according to the numerical value of the doses. Producing this
type of plot allows to clearly determine distinctions between different
dose groups. However, it is wise to produce plots of dose vs response
with dose on a scale proportional to the numerical value of the doses.
This allows to more clearly see the shape of the dose-response
relationship. Drug ABC123 has a nice dose-response curve shape that
resembles a typical Emax model, appearing to plateau around 7.5 change
from baseline in intensity score. <!--END_EXPLANATION--></p>
<pre class="r"><code>gg <- ggplot(pd_single_dosing %>% subset(NOMTIME == 24,),
aes(y = CHG, x = DOSE, group = NOMTIME))
gg <- gg + xgx_stat_ci(conf_level = 0.975,
geom = c("point", "errorbar"),
alpha = 0.5)
gg <- gg + geom_hline(yintercept = 0, color = "red", linetype = "dashed")
gg <- gg + theme(axis.text = element_text(size = 13),
axis.title = element_text(size = 14),
strip.text = element_text(size = 14))
gg <- gg + ggtitle("Single dosing")
gg <- gg + ylab(paste0(pd_label,"\nchange from baseline"))
gg <- gg + xlab(dose_label)
gg <- gg + facet_wrap(~NOMTIME_label, nrow = 1)
gg <- gg + xgx_annotate_status()
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PD_real_example_files/figure-html/unnamed-chunk-5-1.png" width="960" /></p>
</div>
</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] caTools_1.18.2 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
## [10] zoo_1.8-11 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>
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