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<h1 class="title toc-ignore">Safety Plots</h1>
<h4 class="author">Fariba Khanshan</h4>
</div>
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>This document contains plots for safety data as well as the R code
that generates these graphs. These plots are not strictly exploratory
plots as data from a PopPK model are used to generate some of the
plots.</p>
</div>
<div id="setup" class="section level2">
<h2>Setup</h2>
<pre class="r"><code>library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(zoo)
library(xgxr)
#flag for labeling figures as draft
status = "DRAFT"
## ggplot settings
xgx_theme_set()</code></pre>
</div>
<div id="load-dataset" class="section level2">
<h2>Load Dataset</h2>
<p>The plots presented here are based on merged safety data with a popPK
model generated parameters data (<a href="Data/AE_xgx.csv">download
dataset</a>), as well as a dataset containing model-generated AUCs (<a
href="Data/AUC_Safety.csv">download dataset</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/Adverse_Events.Rmd">Rmarkdown-Template</a>.</p>
<pre class="r"><code>auc <- read.csv("../Data/AUC_Safety.csv")
ae.data <- read.csv("../Data/AE_xgx.csv")
ae.data$DOSE_label <- paste(ae.data$Dose,"mg")
ae.data = ae.data %>%
arrange(DOSE_label) %>%
mutate(DOSE_label_low2high = factor(DOSE_label, levels = unique(DOSE_label)),
DOSE_label_high2low = factor(DOSE_label, levels = rev(unique(DOSE_label))))
#ensure dataset has all the necessary columns
ae.data = ae.data %>%
mutate(SUBJID = SUBJID, # ID column
DAY = DAY, # Day of the AE
time = time, # time of the AE in (h)
Dose = Dose, # DOSE column here (numeric value)
AUC = AUC, # Cumulative AUC at AE day
Cmax = Cmax, # Cmax at AE day
Cmin = Cmin, # Cmin at AE day\
AUCDAY1 = AUCDAY1, # AUC at day 1
AUCAVE = AUCAVE, # Average AUC up to the AE day
AETOXGRS = AETOXGRS, # AE grades (Character)
AETOXGRDN = AETOXGRDN, # AE grades (numenric)
AE = AE, # Binary YES/NO AE
)
auc = auc %>%
mutate(SUBJID = SUBJID, # Subject ID
time_hr =time_hr, # Simulation time in (h)
AUC_popPK = AUC_popPK, # Cumulative AUC from popPK model
Cmax_popPK = Cmax_popPK, # Cmax from popPK
Cmin_popPK = Cmin_popPK # Cmin from popPK
)
# add binary 0 and 1 colun to the dataset
ae.data$AE_binary <- as.numeric(as.character( plyr::mapvalues(ae.data$AE,
c('Yes','No'),
c(1, 0)) ))
# AUC dataset for additional plots
ae.data = ae.data %>%
mutate(AUC_bins = cut(AUCAVE, quantile(AUCAVE, na.rm=TRUE), na.rm=TRUE, include.lowest = TRUE)) %>%
group_by(AUC_bins) %>%
mutate(AUC_midpoints = median(AUCAVE))
# Function to add number of subjects in each group
n_fun <- function(x){
return(data.frame(y = median(x)*0.01, label = paste0("n = ",length(x))))
}
#units and labels
time_units_dataset = "hours"
time_units_plot = "days"
trtact_label = "Dose"
dose_label = "Dose (mg)"
time_label = "Time(Days)"
timemonth_label = "Time (months)"
conc_label = "Concentration (ng/ml)"
trough_label = "Trough Concentration (ng/mL)"
conc_units = "ng/ml"
auctau_label = "AUCtau (ng.h/ml)"
ae_label = "AE"
aeprob_label = "Probability of AE (%)"
sld_label = "Percent Change in\nSum of Longest Diameters"
#directories for saving individual graphs
dirs = list(
parent_dir= tempdir(),
rscript_dir = "./",
rscript_name = "Example.R",
results_dir = "./",
filename_prefix = "",
filename = "Example.png")</code></pre>
</div>
<div id="exposure-safety-plots" class="section level2">
<h2>Exposure-safety plots</h2>
<p>These plots are looking at the AUC (or Cmax and Ctrough) for patients
with the AE and for those who don’t have the AE at each dose group. The
number of the patients at each dose level is also included in the plots.
With these type of plots we are looking to see if there is a correlation
(or absence a correlation) between exposure and AE.</p>
<div id="auc-vs-dose-group-by-ae" class="section level3">
<h3>AUC vs Dose group by AE</h3>
<pre class="r"><code>gg <- ggplot(data= ae.data,aes(x=factor(Dose), y=AUCAVE, fill=factor(AE)))
gg <- gg + geom_boxplot(data=ae.data,aes(x=factor(Dose),y=AUCAVE, fill=factor(AE)),outlier.shape = NA)
gg <- gg + geom_point(color="black",binaxis='y', stackdir='center',dotsize=0.5,
position=position_jitterdodge(0.27))
gg <- gg + guides(color=guide_legend(""))
gg <- gg + stat_summary(data=ae.data, fun.data = n_fun, geom = "text",
fun.y=mean, position = position_dodge(width = 0.75))
gg <- gg + scale_fill_discrete(name="AE occurrence:")
gg <- gg + ylab("")
gg <- gg + labs(y=auctau_label,x=dose_label)
gg <- gg + xgx_annotate_status(status)
gg</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-3-1.png" width="768" /></p>
</div>
<div id="explore-ae-vs-auc" class="section level3">
<h3>Explore AE vs AUC</h3>
<p>Plot binary AE occurrence against AUC or Cmax.</p>
<pre class="r"><code>gg <- ggplot(data = ae.data, aes(y=AUCAVE,x=AE))
gg <- gg + geom_jitter(data = ae.data,
aes(color = DOSE_label_high2low), shape=19, width = 0.1, height = 0, alpha = 0.5)
gg <- gg + geom_boxplot(width = 0.5, fill = NA, outlier.shape=NA)
gg <- gg + guides(color=guide_legend(""),fill=guide_legend(""))
gg <- gg + coord_flip()
gg <- gg + labs(y=auctau_label,x=ae_label)
gg <- gg + xgx_annotate_status(status)
gg</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-4-1.png" width="768" /></p>
</div>
</div>
<div id="probability-of-ae-for-each-dose-group" class="section level2">
<h2>Probability of AE for each dose group</h2>
<p>Plot binary AE occurrence against dose. Using summary statistics can
be helpful, e.g. Mean +/- SE, or median, 5th & 95th percentiles. For
binary response data, plot the percent responders along with binomial
confidence intervals.</p>
<p>Here are some questions to ask yourself when looking at Dose-safety
plots: Do you see any relationship? Does AE increase (decrease) with
increasing dose?</p>
<pre class="r"><code>gg1 <- ggplot(data = ae.data, aes(x=Dose,y=AE_binary))
gg1 <- gg1 + geom_smooth( method = "glm",method.args=list(family=binomial(link = logit)), color = "black")
gg1 <- gg1 + xgx_stat_ci(conf_level = 0.95, distribution = "binomial", geom = c("point"), shape = 0, size = 4)
gg1 <- gg1 + xgx_stat_ci(conf_level = 0.95, distribution = "binomial", geom = c("errorbar"), size = 0.5)
gg1 <- gg1 + guides(color=guide_legend(""),fill=guide_legend(""))
gg1 <- gg1 + scale_y_continuous(labels=scales::percent)
gg1 <- gg1 + labs(y=aeprob_label,x=dose_label)
gg1 <- gg1 + xgx_annotate_status(status)
## Same plot but on a log scale
gg2 <- gg1 + xgx_scale_x_log10(breaks=unique(ae.data$Dose))
#put linear and log scale plots side-by-side:
grid.arrange(gg1, gg2, ncol=2)</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-5-1.png" width="768" /></p>
<div id="probability-of-ae-by-auc" class="section level3">
<h3>Probability of AE by AUC</h3>
<p>Plot AE against exposure. Include a logistic regression for binary
data to help determine the shape of the exposuresafety relationship.
Summary information such as mean and 95% confidence intervals by
quartiles of exposure can also be plotted. Exposure and Cmax metric that
you use in these pltots could be either be raw concentrations, or NCA or
model-derived exposure metrics (e.g. Cmin, Cmax, AUC), and may depend on
the level of data that you have available.</p>
<pre class="r"><code>gg <- ggplot(data = ae.data, aes(x=AUCAVE,y=AE_binary))
gg <- gg + geom_jitter(aes( color = DOSE_label_high2low), width = 0, height = 0.05, alpha = 0.5)
gg <- gg + geom_smooth( method = "glm",method.args=list(family=binomial(link = logit)), color = "black")
gg <- gg + xgx_stat_ci(mapping = aes(x = AUC_midpoints, y = AE_binary),
conf_level = 0.95, distribution = "binomial", geom = c("point"), shape = 0, size = 4)
gg <- gg + xgx_stat_ci(mapping = aes(x = AUC_midpoints, y = AE_binary),
conf_level = 0.95, distribution = "binomial", geom = c("errorbar"), size = 0.5)
gg <- gg + guides(color=guide_legend(""),fill=guide_legend(""))
gg <- gg + scale_y_continuous(breaks=c(0,1)) + coord_cartesian(ylim=c(-0.2,1.2))
gg <- gg + labs(x=auctau_label,y=ae_label)
gg <- gg + xgx_annotate_status(status)
gg</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-6-1.png" width="768" /></p>
</div>
</div>
<div id="boxplots-of-exposure-over-time-with-ae-highlighted"
class="section level2">
<h2>Boxplots of Exposure over time, with AE highlighted</h2>
<p>The plot below shows time vs AUC from a pop PK model at each day. The
colored dots corresponding to adverse events. It is not strictly an
exploratory plot. But once you have a PopPK model, it is simple to
generate this plot. Just plot the time vs the predicted AUC and color
the time points red on days that an adverse event occurred.</p>
<pre class="r"><code>data_to_plot <- ae.data %>% subset(!is.na(AETOXGRDN),)
gg <- ggplot()
gg <- gg + geom_jitter(data=auc %>% subset(AUC_day<30,),
aes(x=AUC_day, y=AUC_popPK/AUC_day, group=AUC_day),
color="grey", alpha = 0.5,
position=position_jitter(width=.1, height=0))
gg <- gg + geom_boxplot(data=auc %>% subset(AUC_day<30,),
aes(x=AUC_day, y=AUC_popPK/AUC_day, group=AUC_day),
outlier.shape = NA, fill = NA)
gg <- gg + geom_point(data=data_to_plot %>% subset(DAY<30,),
aes(x=DAY, y=AUC/DAY, color=factor(AETOXGRS)),size=2)
gg <- gg + scale_color_manual(breaks = c("GR1", "GR2", "GR3"),
values=c(rgb(1,0.5,0.5), rgb(0.75,0.25,0.25), rgb(0.5,0,0)))
gg <- gg + guides(color=guide_legend(""),fill=guide_legend(""))
gg <- gg + scale_x_continuous(breaks = seq(0, 30, by = 3))
gg <- gg + xgx_annotate_status(status)
gg <- gg + labs(x=time_label,y=auctau_label)
gg</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-7-1.png" width="768" /></p>
</div>
<div
id="oncology-individual-plots-of-percent-change-from-baseline-including-dosing-history-labeled-by-overall-response-and-ae-grade"
class="section level2">
<h2>Oncology individual plots of percent change from baseline, including
dosing history, labeled by “Overall Response” and AE grade</h2>
<p>These plots allow one to look for subtle trends in the individual
trajectories with respect to the dosing history, safety events and
efficacy as percent change of tunor size from baseline. The plots below
make use of the following oncology datasets: RECIST and nmpk dataset <a
href="Oncology_Efficacy_Data.csv">(download here)</a> and dose record
dataset <a href="Oncology_Efficacy_Dose.csv">(download here)</a></p>
<pre class="r"><code># Read oncology efficacy data from the oncology efficacy
# page and combine them with safety data in this page
safety_data <- read.csv("../Data/AE_xgx.csv")
efficacy_data <- read.csv("../Data/Oncology_Efficacy_Data.csv")
dose_record <- read.csv("../Data/Oncology_Efficacy_Dose.csv")
efficacy_data$DOSE_label <- paste(efficacy_data$DOSE_ABC,"mg")
efficacy_data$DOSE_label <- factor(efficacy_data$DOSE_label,levels = c(paste(unique(efficacy_data$DOSE_ABC),"mg")))
efficacy_data.monotherapy = efficacy_data %>% filter(COMB=="Single")
efficacy_data.combo = efficacy_data %>% filter(COMB=="Combo")
# Dose record data preparation for making step function plot
# in order to show any dose reduction during the study
# the idea is that at each time point, you have both the current dose and the previous dose
# the dplyr::lag command implements this
data_areaStep <- bind_rows(old = dose_record,
new = dose_record %>%
group_by(IDSHORT) %>%
mutate(DOSE = lag(DOSE)),
.id = "source") %>%
arrange(IDSHORT, TIME, source) %>%
ungroup() %>%
mutate(DOSE = ifelse(lag(IDSHORT)!=IDSHORT, NA, DOSE),
TIME = TIME/24) #convert to days
data_areaStep.monotherapy = filter(data_areaStep,COMB=="Single")
# calculate average dose intensity up to the first assessment:
# "TIME==57"" is the first assessment time in this dataset
first.assess.time = 57
dose_record <- dose_record %>%
group_by(IDSHORT) %>%
mutate(ave_dose_intensity = mean(DOSE[TIME/24 < first.assess.time]))
dose_intensity <- dose_record[c("IDSHORT","COMB","ave_dose_intensity")]
dose_intensity <- subset(dose_intensity, !duplicated(IDSHORT))
# This part is optional to label "OR" in the plot
# "OR" can be substituted with other information, such as non-target, new target lesions
# make the OR label for the plot
safety_label <- safety_data %>%
select(SUBJID, DAY, AETOXGRS, Dose)
colnames(safety_label)[2] <- "TIME"
colnames(safety_label)[4] <- "DOSE_ABC"
safety_label$AETOXGRS <- as.character(safety_label$AETOXGRS)
safety_label <- safety_label[!safety_label$AETOXGRS =="",]
efficacy_AE_label <- efficacy_data %>%
select(SUBJID, TIME, psld, DOSE_ABC)
efficacy_AE_label <- merge(safety_label,efficacy_AE_label, by = c("SUBJID", "TIME","DOSE_ABC"),
all.x=T, all.y=T)
subj <- efficacy_AE_label %>%
subset(!is.na(psld)) %>%
group_by(SUBJID) %>%
mutate(CountNonNa = length(psld))
subj <- c(unique(subset(subj, CountNonNa>1, "SUBJID")))
efficacy_AE_label <- efficacy_AE_label %>%
subset(SUBJID%in%subj$SUBJID)%>%
group_by(SUBJID) %>%
mutate(ValueInterp = na.approx(psld,TIME, na.rm=FALSE))
efficacy_AE_label <- efficacy_AE_label[!is.na(efficacy_AE_label$AETOXGRS),]
efficacy_AE_label <- efficacy_AE_label[!is.na(efficacy_AE_label$ValueInterp),]
efficacy_AE_label <- subset( efficacy_AE_label, select = -psld )
colnames(efficacy_AE_label)[5] <- "psld"
colnames(efficacy_AE_label)[1] <- "IDSHORT"
efficacy_data.label <- efficacy_data %>%
group_by(SUBJID) %>%
mutate(label_psld = as.numeric(ifelse(TIME==TIME_OR , psld,""))) %>%
filter(!(is.na(label_psld) | label_psld==""))
dose.shift = 50
dose.scale = 1.2
data_areaStep.monotherapy = data_areaStep.monotherapy %>%
mutate(DOSE.shift = DOSE/dose.scale+dose.shift)
dose.unique = c(0,unique(efficacy_data.monotherapy$DOSE_ABC))
gg <- ggplot(data = efficacy_data.monotherapy)
gg <- gg + geom_point(mapping = aes(y= psld, x= TIME))
gg <- gg + geom_text(data= efficacy_data.label,aes(y= label_psld, x= TIME_OR, label=OR), vjust=-.5)
gg <- gg + geom_hline(aes(yintercept = 0),size=0.1, colour="black")
gg <- gg + geom_line(mapping = aes(y= psld, x= TIME))
gg <- gg + geom_ribbon(data= data_areaStep.monotherapy,
aes( ymin = 50, ymax = DOSE.shift , x= TIME),
fill="palegreen2", color = "black", alpha=0.5 )
gg <- gg + geom_text(data= efficacy_AE_label,
aes(y= psld, x= TIME, label=AETOXGRS), colour="red",fontface=2,
size=5, show.legend = F, hjust=-0.05, vjust=2)
gg <- gg + geom_vline(data= efficacy_AE_label,
aes(xintercept= TIME),
size=1, linetype="dashed", colour="red")
gg <- gg + facet_wrap(~IDSHORT, ncol=6)
gg <- gg + scale_y_continuous(
sec.axis = sec_axis(~(.-dose.shift)*dose.scale, name = "Dose(mg)", breaks = dose.unique))
gg <- gg + labs(y = sld_label, x= timemonth_label)
gg <- gg + xgx_scale_x_time_units(units_dataset = "day", units_plot ="month")
gg <- gg + theme(text = element_text(size=15))
gg <- gg + xgx_annotate_status(status)
gg</code></pre>
<p><img src="Adverse_Events_files/figure-html/unnamed-chunk-8-1.png" width="960" /></p>
</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] xgxr_1.1.1 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] nlme_3.1-160 bitops_1.0-7 RColorBrewer_1.1-3 Deriv_4.1.3 tools_4.1.0 backports_1.4.1 bslib_0.4.0
## [8] utf8_1.2.2 R6_2.5.1 rpart_4.1.16 mgcv_1.8-41 Hmisc_4.7-0 DBI_1.1.3 colorspace_2.0-3
## [15] nnet_7.3-17 withr_2.5.0 tidyselect_1.2.0 Exact_2.1 compiler_4.1.0 cli_3.4.1 htmlTable_2.2.1
## [22] binom_1.1-1 expm_0.999-6 labeling_0.4.2 sass_0.4.2 scales_1.2.1 checkmate_2.1.0 mvtnorm_1.1-3
## [29] readr_2.1.3 proxy_0.4-26 stringr_1.4.1 digest_0.6.30 foreign_0.8-82 rmarkdown_2.17 base64enc_0.1-3
## [36] jpeg_0.1-9 pkgconfig_2.0.3 htmltools_0.5.3 fastmap_1.1.0 highr_0.9 htmlwidgets_1.5.4 rlang_1.0.6
## [43] rstudioapi_0.14 jquerylib_0.1.4 generics_0.1.3 farver_2.1.1 jsonlite_1.8.3 RCurl_1.98-1.4 magrittr_2.0.3
## [50] Formula_1.2-4 interp_1.1-2 Matrix_1.5-1 Rcpp_1.0.9 DescTools_0.99.42 munsell_0.5.0 fansi_1.0.3
## [57] lifecycle_1.0.3 stringi_1.7.8 yaml_2.3.6 MASS_7.3-58.1 rootSolve_1.8.2.2 plyr_1.8.7 grid_4.1.0
## [64] lmom_2.8 deldir_1.0-6 lattice_0.20-45 splines_4.1.0 pander_0.6.4 hms_1.1.2 knitr_1.40
## [71] pillar_1.8.1 boot_1.3-28 gld_2.6.2 codetools_0.2-18 glue_1.6.2 evaluate_0.17 latticeExtra_0.6-30
## [78] data.table_1.14.2 png_0.1-7 vctrs_0.5.0 tzdb_0.3.0 gtable_0.3.1 purrr_0.3.5 assertthat_0.2.1
## [85] cachem_1.0.6 xfun_0.34 e1071_1.7-8 class_7.3-19 survival_3.4-0 minpack.lm_1.2-1 tibble_3.1.8
## [92] cluster_2.1.3 ellipsis_0.3.2</code></pre>
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