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<h1 class="title toc-ignore">PK/PD, Exposure-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_PKPD_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
PK_CMT = 2
PD_CMT = 4
SS_PROFDAY = 6 # steady state prof day
PD_PROFDAYS <- c(0, 2, 4, 6)
TAU = 24 # time between doses, units should match units of TIME, e.g. 24 for QD, 12 for BID, 7*24 for Q1W (when units of TIME are h)
#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 = 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
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_NORM = LIDV/DOSE,
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 pk dataset
pk_data <- pkpd_data %>%
filter(CMT == PK_CMT)
#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)))))
#create wide pkpd dataset for plotting PK vs PD
pkpd_data_wide = pd_data %>%
select(ID, NOMTIME, PD = LIDV) %>%
right_join(pk_data) %>%
rename(CONC = LIDV) %>%
filter(!is.na(PD))%>%
filter(!is.na(CONC))
#perform NCA, for additional plots
NCA = pk_data %>%
group_by(ID, DOSE) %>%
filter(!is.na(LIDV)) %>%
summarize(AUC_0 = ifelse(length(LIDV[NOMTIME > 0 & NOMTIME <= TAU]) > 1,
caTools::trapz(TIME[NOMTIME > 0 & NOMTIME <= TAU],
LIDV[NOMTIME > 0 & NOMTIME <= TAU]),
NA),
Cmax_0 = ifelse(length(LIDV[NOMTIME > 0 & NOMTIME <= TAU]) > 1,
max(LIDV[NOMTIME > 0 & NOMTIME <= TAU]),
NA),
AUC_tau = ifelse(length(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]) > 1,
caTools::trapz(TIME[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)],
LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]),
NA),
Cmax_tau = ifelse(length(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]) > 1,
max(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]),
NA),
SEX = SEX[1], #this part just keeps the SEX and WEIGHTB covariates
WEIGHTB = WEIGHTB[1]) %>%
gather(PARAM, VALUE, -c(ID, DOSE, SEX, WEIGHTB)) %>%
ungroup() %>%
mutate(VALUE_NORM = VALUE/DOSE,
PROFDAY = ifelse(PARAM %in% c("AUC_0", "Cmax_0"), 1, SS_PROFDAY))
# Merge with PD data
# add response data at day 1 and steady state to NCA for additional plots
NCA <- pd_data %>% subset(PROFDAY %in% c(1, SS_PROFDAY),) %>%
select(ID, PROFDAY, DAY_label, PD = LIDV, TRTACT_low2high, TRTACT_high2low) %>%
merge(NCA, by = c("ID","PROFDAY"))
#units and labels
time_units_dataset = "hours"
time_units_plot = "days"
trtact_label = "Dose"
dose_units = unique((pkpd_data %>% filter(CMT == DOSE_CMT) )$LIDV_UNIT) %>% as.character()
dose_label = paste0("Dose (", dose_units, ")")
conc_units = unique(pk_data$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_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.
Consider either coloring by dose or faceting by dose. Depending on the
amount of data one graph may be better than the other.
<!--END_EXPLANATION--></p>
<div
id="pk-and-pd-marker-over-time-colored-by-dose-median-5th-95th-percentiles-by-nominal-time"
class="section level3">
<h3>PK and PD marker over time, colored by Dose, median, 5th, 95th
percentiles by nominal time</h3>
<!--START_EXPLANATION-->
<p>Does the effect appear to increase and decrease quickly on a short
time scale, or does is occur over a longer time scale? Do the PK and PD
profiles appear to be on the same time scale, or does the PD seem
delayed compared to the PK? Is there clear separation between the
profiles for different doses? Does the effect appear to increase with
increasing dose? Do you detect a saturation of the effect?
<!--END_EXPLANATION--></p>
<pre class="r"><code>gg <- ggplot(data = pk_data,
aes(x = NOMTIME, y=LIDV, color = TRTACT_high2low, fill = TRTACT_high2low))
gg <- gg + xgx_geom_pi(percent_level = 0.95, geom = c("errorbar", "point", "line"),
position = position_dodge(width = 12), alpha = 0.5)
gg <- gg + xgx_scale_y_log10()
gg <- gg + ylab(conc_label)
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-3-1.png" width="960" /></p>
<pre class="r"><code>gg %+% (data = pd_data) + ylab(pd_label) + scale_y_continuous()</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-3-2.png" width="960" /></p>
</div>
<div
id="pk-and-pd-marker-over-time-faceted-by-dose-median-5th-95th-percentiles-by-nominal-time"
class="section level3">
<h3>PK and PD marker over time, faceted by Dose, median, 5th, 95th
percentiles by nominal time</h3>
<pre class="r"><code>gg + facet_grid(.~TRTACT_low2high) + aes(color = NULL) + theme(legend.position = "none")</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-4-1.png" width="960" /></p>
<pre class="r"><code>gg %+% (data = pd_data %>% subset(DOSE > 0)) +
ylab(pd_label) + scale_y_continuous() +
facet_grid(.~TRTACT_low2high) +
aes(color = NULL) +
theme(legend.position = "none")</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-4-2.png" width="960" /></p>
</div>
</div>
<div id="explore-variability" class="section level2">
<h2>Explore Variability</h2>
<div
id="pk-and-pd-marker-over-time-colored-by-dose-dots-lines-grouped-by-individuals"
class="section level3">
<h3>PK and PD marker over time, colored by Dose, dots & lines
grouped by individuals</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data,
aes(x = TIME, y = LIDV, color = TRTACT_high2low, group = ID))
gg <- gg + geom_point(alpha = 0.5)
gg <- gg + geom_line(alpha = 0.5)
gg <- gg + xgx_scale_y_log10()
gg <- gg + ylab(conc_label)
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xgx_scale_x_time_units(units_dataset = "h", units_plot = "d")
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-5-1.png" width="960" /></p>
<pre class="r"><code>gg %+% (data = pd_data) +
ylab(pd_label) + scale_y_continuous()</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-5-2.png" width="960" /></p>
</div>
<div
id="pk-and-pd-marker-over-time-faceted-by-dose-dots-lines-grouped-by-individuals"
class="section level3">
<h3>PK and PD marker over time, faceted by Dose, dots & lines
grouped by individuals</h3>
<pre class="r"><code>gg + facet_grid(.~TRTACT_low2high) + aes(color = NULL) + theme(legend.position = "none")</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-6-1.png" width="960" /></p>
<pre class="r"><code>gg %+% (data = pd_data %>% subset(DOSE > 0)) +
ylab(pd_label) + scale_y_continuous() +
facet_grid(.~TRTACT_low2high) +
aes(color = NULL) +
theme(legend.position = "none")</code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-6-2.png" width="960" /></p>
</div>
</div>
<div id="explore-exposure-response-relationship" class="section level2">
<h2>Explore Exposure-Response Relationship</h2>
<pre class="r"><code>gg <- ggplot(data = pkpd_data_wide, aes(x = CONC,y = PD))
gg <- gg + geom_jitter(aes(color = TRTACT_high2low), width = 0, height = 0.1, alpha = 0.5)
gg <- gg + ylab(pd_label) + xlab(conc_label)
gg <- gg + guides(color = guide_legend(""))
gg <- gg + geom_smooth(method = "glm", method.args = list(family=poisson), color = "black")
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-7-1.png" width="960" /></p>
<pre class="r"><code>gg <- ggplot(data = pkpd_data_wide %>% subset(PROFDAY %in% PD_PROFDAYS),
aes(y = CONC, x = PD))
gg <- gg + geom_boxplot(aes(group = PD)) + coord_flip()
gg <- gg + geom_jitter(aes(color = TRTACT_high2low), width = 0.1, height = 0, alpha = 0.5)
gg <- gg + guides(color = guide_legend(""), fill = guide_legend(""))
gg <- gg + xlab(pd_label) + ylab(conc_label)
gg <- gg + xgx_scale_y_log10()
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_PKPD_count_files/figure-html/unnamed-chunk-8-1.png" width="768" /></p>
<div id="explore-covariate-effects-on-exposure-response-relationship"
class="section level3">
<h3>Explore covariate effects on Exposure-Response Relationship</h3>
<!--START_EXPLANATION-->
<p>Stratify exposure-response plots by covariates of interest to explore
whether any key covariates impact response independent of exposure. For
examples of plots and code startifying by covariate, see <a
href="Multiple_Ascending_Dose_PKPD_continuous.html#explore_covariate_effects_on_exposure-response_relationship">Continuous
PKPD Covariate Section</a></p>
</div>
<div id="individual-response-vs-exposure-hysteresis-plots"
class="section level3">
<h3>Individual response vs exposure hysteresis plots</h3>
<p>Using geom_path you can create hysteresis plots of response vs
exposure. Including details like arrows or colors can be helpful to
indicate the direction of time.</p>
<p>If most of the arrows point up and to the right or down and to the
left, this indicates a positive relationship between exposure and
response (i.e. increasing exposure -> increasing response). Arrows
pointing down and to the right or up and to the left indicate a negative
relationship.</p>
<p>In a hysteresis plot, you want to determine whether the path is
curving, and if so in what direction. If you detect a curve in the
hysteresis plot, this indicates there is a delay between the exposure
and the response. Normally, a clockwise turn indicates that increasing
exposure is associated with (a delayed) increasing response, while a
counter clockwise turn indicates increasing concentration gives (a
delayed) decreasing response.</p>
<p>In the plots below, most of the hysteresis paths follow a counter
clockwise turn, with most arrows pointing up and to the right or down
and to the left, indicating the effect increases in a delayed manner
with increasing concentration. <!--END_EXPLANATION--></p>
<pre class="r"><code>pkpd_data_wide <- pkpd_data_wide %>% arrange(ID, TIME)
gg <- ggplot(data = pkpd_data_wide, aes(x = CONC, y = PD, color = TIME))
gg <- gg + geom_path(arrow = arrow(length=unit(0.15,"cm")))
gg <- gg + xgx_scale_x_log10() + xgx_scale_y_log10()
gg <- gg + ylab(pd_label) + xlab(conc_label)
gg <- gg + facet_wrap(~ID + TRTACT_high2low, ncol = 5)
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)
gg <- gg + xgx_annotate_filenames(dirs)
gg </code></pre>
<p><img src="Multiple_Ascending_Dose_PKPD_count_files/figure-html/unnamed-chunk-9-1.png" width="768" /></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] survminer_0.4.9 ggpubr_0.4.0 survival_3.4-0 knitr_1.40 broom_1.0.1 caTools_1.18.2 DT_0.26 forcats_0.5.2 stringr_1.4.1
## [10] purrr_0.3.5 readr_2.1.3 tibble_3.1.8 tidyverse_1.3.2 xgxr_1.1.1 zoo_1.8-11 gridExtra_2.3 tidyr_1.2.1 dplyr_1.0.10
## [19] ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] googledrive_2.0.0 colorspace_2.0-3 ggsignif_0.6.2 deldir_1.0-6 rio_0.5.27 ellipsis_0.3.2 class_7.3-19
## [8] htmlTable_2.2.1 markdown_1.2 base64enc_0.1-3 fs_1.5.2 gld_2.6.2 rstudioapi_0.14 proxy_0.4-26
## [15] farver_2.1.1 Deriv_4.1.3 fansi_1.0.3 mvtnorm_1.1-3 lubridate_1.8.0 xml2_1.3.3 codetools_0.2-18
## [22] splines_4.1.0 cachem_1.0.6 rootSolve_1.8.2.2 Formula_1.2-4 jsonlite_1.8.3 km.ci_0.5-2 binom_1.1-1
## [29] cluster_2.1.3 dbplyr_2.2.1 png_0.1-7 compiler_4.1.0 httr_1.4.4 backports_1.4.1 assertthat_0.2.1
## [36] Matrix_1.5-1 fastmap_1.1.0 gargle_1.2.1 cli_3.4.1 prettyunits_1.1.1 htmltools_0.5.3 tools_4.1.0
## [43] gtable_0.3.1 glue_1.6.2 lmom_2.8 Rcpp_1.0.9 carData_3.0-4 cellranger_1.1.0 jquerylib_0.1.4
## [50] vctrs_0.5.0 nlme_3.1-160 crosstalk_1.2.0 xfun_0.34 openxlsx_4.2.4 rvest_1.0.3 lifecycle_1.0.3
## [57] rstatix_0.7.0 googlesheets4_1.0.1 MASS_7.3-58.1 scales_1.2.1 hms_1.1.2 expm_0.999-6 RColorBrewer_1.1-3
## [64] curl_4.3.3 yaml_2.3.6 Exact_2.1 KMsurv_0.1-5 pander_0.6.4 sass_0.4.2 rpart_4.1.16
## [71] reshape_0.8.8 latticeExtra_0.6-30 stringi_1.7.8 highr_0.9 e1071_1.7-8 checkmate_2.1.0 zip_2.2.0
## [78] 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
## [85] 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
## [92] 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
## [99] mgcv_1.8-41 abind_1.4-5 RCurl_1.98-1.4 nnet_7.3-17 car_3.0-11 crayon_1.5.2 modelr_0.1.9
## [106] survMisc_0.5.5 interp_1.1-2 utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.17 progress_1.2.2 jpeg_0.1-9
## [113] grid_4.1.0 readxl_1.4.1 minpack.lm_1.2-1 data.table_1.14.2 reprex_2.0.2 digest_0.6.30 xtable_1.8-4
## [120] munsell_0.5.0 bslib_0.4.0</code></pre>
</div>
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