PD - Multiple Ascending Dose - Realistic Data

Overview

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.

This code makes use of the following files which can be downloaded from the available links

• blinded dataset of PK and PD endpoints

Data specifications can be accessed on Datasets and Rmarkdown template to generate this page can be found on Rmarkdown-Template.

Setup

# remove reference to home directory in libPaths
.libPaths(grep("home", .libPaths(), value=TRUE, invert=TRUE))
.libPaths(grep("usr", .libPaths(), value=TRUE, invert=TRUE))

# add localLib to libPaths for locally installed packages
.libPaths(c("localLib", .libPaths()))
# will load from first filepath first, then look in .libPaths for more packages not in first path
# version matches package in first filepath, in the case of multiple instances of a package

# library(rmarkdown)
library(gridExtra)
library(grid)
library(ggplot2)
library(dplyr)
library(RxODE)
library(caTools)

theme_set(theme_bw(base_size=12))

Loading dataset

data_all<- read.csv(file="../Data/mt12345.csv", header=T)
##Remove data from Part 3 and placebo
data_all<-data_all[data_all$ID<1109|data_all$ID>1117,]

##Remove negative times
data_all<-data_all[data_all$NTIM>=0,]

##Remove duplicates
data_all<-data_all[!duplicated(data_all),]

##Find multiple_dosing IDs 
IDs<-unique(data_all$ID)
multiple_dosing_IDs<-NULL
single_dosing_IDs<-NULL
k<-0
l<-0
for (i in IDs){
  if (length(which(data_all$EVID[data_all$ID==i]==1))>1){
    k<-k+1
    multiple_dosing_IDs[k]<-i
  }else{l<-l+1
        single_dosing_IDs[l]<-i}
}

##Take info for generating the dosing times
data_dosing<-NULL
data_dosing$ID<-data_all$ID[data_all$EVID==1&data_all$RNDDOSE!=0]
data_dosing$NTIM<-data_all$NTIM[data_all$EVID==1&data_all$RNDDOSE!=0]
data_dosing$DV<-12000
data_dosing$DOSE<-data_all$DOSE[data_all$EVID==1&data_all$RNDDOSE!=0]
data_dosing<-as.data.frame(data_dosing)

data_all$DAY = data_all$NTIM/24

Intensity Score (a Composite Score)

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.

data_to_plot <- data_all  

##Define and name the compartment
cmt_number<-13
cmt_name<-"Intensity Score"
units<-''

##create column with change from baseline
data_to_plot$LIDV2<-data_to_plot$LIDV
for (i in 1:nrow(data_to_plot)){
  if (data_to_plot$CMT[i]==13){
    data_to_plot$LIDV2[i]<-data_to_plot$LIDV2[i]-
                             (data_to_plot$LIDV[data_to_plot$ID==data_to_plot$ID[i]&data_to_plot$NTIM==0&data_to_plot$CMT==13])}
  
}

##Find multiple_dosing IDs
data_to_plot_single_dosing<-data_to_plot
data_to_plot_multiple_dosing<-data_to_plot
for (i in 1:k){data_to_plot_single_dosing  <-data_to_plot_single_dosing[data_to_plot_single_dosing$ID!=multiple_dosing_IDs[i],]}
for (i in 1:l){data_to_plot_multiple_dosing<-data_to_plot_multiple_dosing[data_to_plot_multiple_dosing$ID!=  single_dosing_IDs[i],]}

##Take info for generating the dosing times
data_dosing_single_dosing<-data_dosing
data_dosing_multiple_dosing<-data_dosing
for (i in 1:k){data_dosing_single_dosing<-data_dosing_single_dosing[data_dosing_single_dosing$ID!=multiple_dosing_IDs[i],]}
for (i in 1:l){data_dosing_multiple_dosing<-data_dosing_multiple_dosing[data_dosing_multiple_dosing$ID!=single_dosing_IDs[i],]}


data_to_plot                <- data_to_plot  [data_to_plot$CENS==0&data_to_plot$CMT==cmt_number,]
data_to_plot_single_dosing  <- data_to_plot_single_dosing  [data_to_plot_single_dosing$CENS==0&data_to_plot_single_dosing$CMT==cmt_number,]
data_to_plot_multiple_dosing<- data_to_plot_multiple_dosing[data_to_plot_multiple_dosing$CENS==0&data_to_plot_multiple_dosing$CMT==cmt_number,]
##remove dose=100mg as there is only zero time for that.
data_to_plot_multiple_dosing<-data_to_plot_multiple_dosing[data_to_plot_multiple_dosing$DOSE!=100,]

labelFun <- function(variable,value){     
  paste0(value," mg")                             
}

ytitle<-ylab(paste0(cmt_name," ",units,"\n change from baseline"))
xtitle<-xlab("Time (days)")

plot2<-ggplot(data_to_plot_multiple_dosing, aes(y=LIDV2, x= DAY, group=NTIM)) + 
  geom_boxplot(width = 5)+
  ggtitle("Multiple dosing")+ 
  theme_bw() +
  ytitle+
  xtitle+
  geom_hline(yintercept=0,color="red",linetype="dashed")+
  scale_x_continuous(breaks = c(-1,7,14))+
  theme(axis.text=element_text(size=13),axis.title=element_text(size=14),strip.text=element_text(size=14))+
  facet_wrap(~DOSE,nrow=1,label=labelFun)

plot2

labelFun <- function(variable,value){     
  paste0(value," h")                             
}

ytitle<-ylab(paste0(cmt_name," ",units,"\n change from baseline"))
xtitle<-xlab("Dose (mg)")

plot1<-ggplot(data_to_plot_single_dosing, aes(y=LIDV2, x= factor(DOSE), group=NTIM)) + 
  geom_boxplot(aes(group = factor(DOSE)))+
  ggtitle("Single dosing")+ 
  theme_bw() +
  ytitle+
  xtitle+
  geom_hline(yintercept=0,color="red",linetype="dashed")+
  theme(axis.text=element_text(size=13),axis.title=element_text(size=14),strip.text=element_text(size=14))+
  facet_wrap(~NTIM,nrow=1,label=labelFun)

plot1 

labelFun <- function(variable,value){     
  paste0(value," h")                             
}

plot1b<-ggplot(data_to_plot_single_dosing[data_to_plot_single_dosing$NTIM==24,], aes(y=LIDV2, x= DOSE, group=NTIM)) + 
  stat_summary(aes(group = factor(DOSE)), geom = "point", fun.y = median, alpha = 0.5)+
  stat_summary(aes(group = factor(DOSE)), geom = "errorbar",fun.data = function(y){data.frame(ymin = quantile(y,0.025), ymax = quantile(y, 0.975))}, alpha = 0.5)+
  ggtitle("Single dosing")+ 
  theme_bw() +
  ytitle+
  xtitle+
  geom_hline(yintercept=0,color="red",linetype="dashed")+
  #scale_x_log10(breaks = unique(data_to_plot_single_dosing$DOSE))+
  theme(axis.text=element_text(size=13),axis.title=element_text(size=14),strip.text=element_text(size=14))+
  facet_wrap(~NTIM,nrow=1,label=labelFun)

plot1b 

R Session Info

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server 7.4 (Maipo)
## 
## Matrix products: default
## BLAS/LAPACK: /CHBS/apps/intel/17.4.196/compilers_and_libraries_2017.4.196/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] DT_0.2         RxODE_0.6-1    bindrcpp_0.2   haven_1.1.0   
##  [5] readr_1.1.1    readxl_1.0.0   xtable_1.8-2   tidyr_0.7.2   
##  [9] caTools_1.17.1 zoo_1.8-0      dplyr_0.7.4    ggplot2_2.2.1 
## [13] gridExtra_2.3 
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.14       RColorBrewer_1.1-2 cellranger_1.1.0  
##  [4] compiler_3.4.3     pillar_1.0.1       plyr_1.8.4        
##  [7] bindr_0.1          forcats_0.2.0      bitops_1.0-6      
## [10] tools_3.4.3        digest_0.6.13      jsonlite_1.5      
## [13] memoise_1.1.0      evaluate_0.10.1    tibble_1.4.1      
## [16] gtable_0.2.0       lattice_0.20-35    pkgconfig_2.0.1   
## [19] rlang_0.1.6        rex_1.1.2          yaml_2.1.16       
## [22] stringr_1.2.0      knitr_1.18         hms_0.4.0         
## [25] htmlwidgets_0.9    rprojroot_1.3-1    glue_1.2.0        
## [28] R6_2.2.2           binom_1.1-1        rmarkdown_1.8     
## [31] reshape2_1.4.3     purrr_0.2.4        magrittr_1.5      
## [34] codetools_0.2-15   backports_1.1.2    scales_0.5.0      
## [37] htmltools_0.3.6    rsconnect_0.8.5    assertthat_0.2.0  
## [40] colorspace_1.3-2   labeling_0.3       stringi_1.1.3     
## [43] lazyeval_0.2.1     munsell_0.4.3      markdown_0.8