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PKPD_Datasets.html
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<title>PKPD Datasets</title>
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Data
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<a href="PKPD_Datasets.html">Master PK/PD Datasets used in creating example plots</a>
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Dose-PK/Exposure
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<a href="Multiple_Ascending_Dose_PK_KeyPlots.html">Example using realistic data</a>
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<h1 class="title toc-ignore">PKPD Datasets</h1>
<h4 class="author">Alison Margolskee</h4>
</div>
<div id="overview" class="section level2">
<h2>Overview</h2>
<p>This page describes the master PKPD analysis datasets used in the
majority of the plots on this site. The datasets were generated using
RxODE. Data specifications can be accessed on <a
href="Datasets.html">Datasets</a>. One dataset was created for single
ascending dose PK and another dataset for multiple ascending dose PK and
PD. Explore the datasets in the tables below, or download from the
following links:</p>
<ul>
<li><a href="Data/Single_Ascending_Dose_Dataset2.csv">simulated dataset
of single ascending dose PK</a></li>
<li><a href="Data/Multiple_Ascending_Dose_Dataset2.csv">master PKPD
dataset for multiple ascending dose PK and PD</a></li>
</ul>
</div>
<div id="setup" class="section level2">
<h2>Setup</h2>
<pre class="r"><code>library(dplyr)
library(RxODE)</code></pre>
</div>
<div id="dataset-generation" class="section level2">
<h2>Dataset Generation</h2>
<div id="indirect-response-system" class="section level3">
<h3>Indirect response system</h3>
<pre class="r"><code>ode <- "
Concentration = centr/V2;
C3 = peri/V3;
C4 = peri2/V4;
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot - (CL+Q+Q2)*Concentration + Q*C3 + Q2*C4;
d/dt(peri) = Q*Concentration - Q*C3;
d/dt(peri2) = Q2*Concentration - Q2*C4;
d/dt(eff) = Kin*(1+Emax*Concentration/(EC50 + Concentration)) - Kout*(1)*eff;
"
work <- tempfile("Rx_intro-")
mod1 <- RxODE(model = ode, modName = "mod1")
cat(gsub(pattern="\nConcentration",replacement="Concentration",x=gsub(pattern=";\n",replacement=" \n", x = ode)))</code></pre>
<pre><code>## Concentration = centr/V2
## C3 = peri/V3
## C4 = peri2/V4
## d/dt(depot) = -KA*depot
## d/dt(centr) = KA*depot - (CL+Q+Q2)*Concentration + Q*C3 + Q2*C4
## d/dt(peri) = Q*Concentration - Q*C3
## d/dt(peri2) = Q2*Concentration - Q2*C4
## d/dt(eff) = Kin*(1+Emax*Concentration/(EC50 + Concentration)) - Kout*(1)*eff</code></pre>
</div>
<div id="single-ascending-dose-pk-dataset" class="section level3">
<h3>Single Ascending Dose PK Dataset</h3>
<pre class="r"><code>set.seed(12345666)
ndose <- 5
nsub <- 10 # number of subproblems
SEX <- rep(c(rep("Male",ceiling(nsub/2)),rep("Female",floor(nsub/2))),ndose)
WT0 <- (SEX=="Male")*runif(length(SEX),60,110)+(SEX=="Female")*runif(length(SEX),50,100)
VCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CLCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CL <- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA <- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 <- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV
DOSE <- sort(rep(c(100,200,400,800,1600),nsub))
theta.all <-
cbind(KA=KA, CL=CL, V2=V2,
Q=10*CLCOV, V3=100*VCOV, V4 = 10000*VCOV, Q2 = 10*CLCOV,
Kin=0.5, Kout=0.05, EC50 = 1, Emax = 4)
# Set nominal times for sampling schedule
SAMPLING <- c(-0.1,0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9)
DOSING <- c(0)
NT <- data.frame(NT = c(SAMPLING, DOSING), label = c(rep("SAMPLING",length(SAMPLING)),rep("DOSING",length(DOSING))))
NT <- NT[order(NT$NT),]
ev.all <- list()
for(idose in 1:length(DOSE)){
ev.all[[idose]] <- eventTable(amount.units='mg', time.units='hours')
# ev.all[[idose]]$add.dosing(dose = DOSE[idose], nbr.doses = 1, dosing.interval = 24)
# ev.all[[idose]]$add.sampling(c(c(-0.1,0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9)))
# Include some noise in the sampling & dosing schedules
temp <- NT
temp$time <- cumsum(c(temp$NT[1] + 0.1*rnorm(1), abs(temp$NT[2:length(temp$NT)]-temp$NT[1:length(temp$NT)-1] + 0.1*rnorm(length(temp$NT)-1))))
temp$time <- temp$time - temp$time[temp$NT==0] # center at first dose
ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label=="DOSING"])
ev.all[[idose]]$add.sampling(temp$time[temp$label=="SAMPLING"])
}
x.all.df <- NULL
ID = 0
for(i in 1:length(ev.all)){
# for(i in 1:nsub){
ID = ID + 1
theta <- theta.all[i,]
inits <- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta["Kin"]/theta["Kout"])
x <- mod1$solve(theta, ev.all[[i]], inits = inits)
x.df <- data.frame(x)
x.df$NT <- SAMPLING
temp <- data.frame(ev.all[[i]]$get.dosing())
temp$NT <- DOSING
temp[,setdiff(names(x.df),names(temp))]<- NA
x.df[,setdiff(names(temp),names(x.df))]<- NA
x.df <- rbind(x.df,temp)
x.df$ID <- ID
x.df$DOSE <- DOSE[i]
x.df$WT0 <- WT0[i]
x.df$SEX <- SEX[i]
if(is.null(x.all.df)){
x.all.df <- x.df
}else{
x.all.df <- rbind(x.all.df, x.df)
}
}</code></pre>
<pre><code>## Warning:
## with negative times, compartments initialize at first negative observed time
## with positive times, compartments initialize at time zero
## use 'rxSetIni0(FALSE)' to initialize at first observed time
## this warning is displayed once per session</code></pre>
<pre class="r"><code>x.all.df$DV <- x.all.df$Concentration*(exp(rnorm(length(x.all.df$Concentration),0,0.6^2))) +
0.01*rnorm(length(x.all.df$Concentration))
x.all.df$DV[x.all.df$DV<0.05]=0.05
x.all.df$DV[x.all.df$Concentration==0]=NA
x.all.df$BLQ=0
x.all.df$BLQ[x.all.df$DV==0.05]=1
my.data <- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT <- 1
my.data$CMT_label <- "Dosing"
temp <- x.all.df[is.na(x.all.df$amt),]
temp$CMT <- 2
temp$CMT_label <- "PK Concentration"
my.data <- rbind(my.data,temp)
my.data$DV <- signif(my.data$DV,3)
my.data$WT0 <- signif(my.data$WT0,3)
my.data$time <- round(my.data$time,3)
my.data <- my.data[order(my.data$ID,my.data$time, my.data$CMT),]
my.data$DOSE_label <- paste(my.data$DOSE,"mg")
my.data$DOSE_label[my.data$DOSE==0] <- "Placebo"
my.data$DOSE_label <- factor(my.data$DOSE_label,levels = c("Placebo",paste(unique(my.data$DOSE[my.data$DOSE!=0]),"mg")))
Single_Ascending_Dose_Dataset <- my.data[,c("ID","time","NT","amt","DV","CMT","CMT_label","BLQ","evid","WT0","SEX","DOSE","DOSE_label")]
write.csv(Single_Ascending_Dose_Dataset,"../Data/Single_Ascending_Dose_Dataset.csv",row.names = FALSE)
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset
Single_Ascending_Dose_Dataset2$PROFTIME <- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$NOMTIME <- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$YTYPE <- Single_Ascending_Dose_Dataset2$CMT
Single_Ascending_Dose_Dataset2$amt[is.na(Single_Ascending_Dose_Dataset2$amt)] <- 0
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset2[!(Single_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Single_Ascending_Dose_Dataset2$CMT==2),]
Single_Ascending_Dose_Dataset2$BLQ[Single_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Single_Ascending_Dose_Dataset2$CMT==2] <- 1L
Single_Ascending_Dose_Dataset2$evid <- plyr::mapvalues(Single_Ascending_Dose_Dataset2$evid, c(NA,101), c(0,1))</code></pre>
<pre><code>## The following `from` values were not present in `x`: 101</code></pre>
<pre class="r"><code>Single_Ascending_Dose_Dataset2$TIMEUNIT <- "Hours"
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==1] <- "mg"
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==2] <- "ng/mL"
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset2[,c("ID","time","NOMTIME","TIMEUNIT","amt","DV","CMT",
"CMT_label","EVENTU","BLQ","evid","WT0","SEX","DOSE_label",
"DOSE")]
names(Single_Ascending_Dose_Dataset2) <- c("ID","TIME","NOMTIME","TIMEUNIT","AMT","LIDV","CMT",
"NAME","EVENTU","CENS","EVID","WEIGHTB","SEX","TRTACT",
"DOSE")
write.csv(Single_Ascending_Dose_Dataset2,"../Data/Single_Ascending_Dose_Dataset2.csv",row.names = FALSE)</code></pre>
<pre class="r"><code>DT::datatable(Single_Ascending_Dose_Dataset2, rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE)) </code></pre>
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class=\"display\">\n <thead>\n <tr>\n <th>ID<\/th>\n <th>TIME<\/th>\n <th>NOMTIME<\/th>\n <th>TIMEUNIT<\/th>\n <th>AMT<\/th>\n <th>LIDV<\/th>\n <th>CMT<\/th>\n <th>NAME<\/th>\n <th>EVENTU<\/th>\n <th>CENS<\/th>\n <th>EVID<\/th>\n <th>WEIGHTB<\/th>\n <th>SEX<\/th>\n <th>TRTACT<\/th>\n <th>DOSE<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":20,"autoWidth":true,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[0,1,2,4,5,6,9,10,11,14]}],"order":[],"orderClasses":false,"lengthMenu":[10,20,25,50,100]}},"evals":[],"jsHooks":[]}</script>
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<div id="multiple-ascending-dose-pk-pd-dataset" class="section level3">
<h3>Multiple Ascending Dose PK & PD Dataset</h3>
<pre class="r"><code>set.seed(12345666)
ndose <- 6
nsub <- 10 # number of subproblems
SEX <- rep(c(rep("Male",ceiling(nsub/2)),rep("Female",floor(nsub/2))),ndose)
WT0 <- (SEX=="Male")*runif(length(SEX),60,110)+(SEX=="Female")*runif(length(SEX),50,100)
VCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CLCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CL <- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA <- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 <- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV
Kin <- 0.1*exp(rnorm(nsub*ndose,0,0.4^2))
DOSE <- sort(rep(c(0,100,200,400,800,1600),nsub))
theta.all <-
cbind(KA=KA, CL=CL, V2=V2,
Q=10*CLCOV, V3=100*VCOV, V4 = 10000*VCOV, Q2 = 10*CLCOV,
Kin=Kin, Kout=0.01, EC50 = 1, Emax = 2+2*(SEX=="Female"))
# Nominal times for sampling schedule
SAMPLING <- c(c(-24,-0.1,0.1,0.5,1,2,4,8,12,18,23.9),23.9+seq(1,4)*24,
5*24 + c(0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9,95.9))
DOSING <- seq(0,5*24,24)
NT <- data.frame(NT = c(SAMPLING, DOSING), label = c(rep("SAMPLING",length(SAMPLING)),rep("DOSING",length(DOSING))))
NT <- NT[order(NT$NT),]
ev.all <- list()
for(idose in 1:length(DOSE)){
ev.all[[idose]] <- eventTable(amount.units='mg', time.units='hours')
# ev.all[[idose]]$add.dosing(dose = DOSE[idose], nbr.doses = 6, dosing.interval = 24)
# ev.all[[idose]]$add.sampling(c(c(-24,-0.1,0.1,0.5,1,2,4,8,12,18,23.9),23.9+seq(1,4)*24,
# 5*24 + c(0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9,95.9)))
# Include some noise in the sampling & dosing schedules
temp <- NT
temp$time <- cumsum(c(temp$NT[1] + 0.1*rnorm(1), abs(temp$NT[2:length(temp$NT)]-temp$NT[1:length(temp$NT)-1] + 0.1*rnorm(length(temp$NT)-1))))
temp$time <- temp$time - temp$time[temp$NT==0] # center at first dose
if(DOSE[idose] > 0){
ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label=="DOSING"])
}else{
ev.all[[idose]]$add.dosing(dose = 1e-12, start.time = temp$time[temp$label=="DOSING"])
}
ev.all[[idose]]$add.sampling(temp$time[temp$label=="SAMPLING"])
}
x.all.df <- NULL
ID = 0
for(i in 1:length(ev.all)){
ID = ID + 1
theta <- theta.all[i,]
inits <- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta["Kin"]/theta["Kout"])
x <- mod1$solve(theta, ev.all[[i]], inits = inits)
x.df <- data.frame(x)
x.df$NT <- SAMPLING
temp <- data.frame(ev.all[[i]]$get.dosing())
temp$NT <- DOSING
temp[,setdiff(names(x.df),names(temp))]<- NA
x.df[,setdiff(names(temp),names(x.df))]<- NA
x.df <- rbind(x.df,temp)
x.df$ID <- ID
x.df$DOSE <- DOSE[i]
x.df$WT0 <- WT0[i]
x.df$SEX <- SEX[i]
if(is.null(x.all.df)){
x.all.df <- x.df
}else{
x.all.df <- rbind(x.all.df, x.df)
}
}
x.all.df$DV <- x.all.df$Concentration*(exp(rnorm(length(x.all.df$Concentration),0,0.6^2))) +
0.01*rnorm(length(x.all.df$Concentration))
x.all.df$DV[x.all.df$DV<0.05]=0.05
x.all.df$DV[x.all.df$Concentration==0]=NA
x.all.df$BLQ <- 0
x.all.df$BLQ[x.all.df$DV==0.05] <- 1
temp <- exp(rnorm(length(x.all.df$eff),0,0.4^2))
x.all.df$eff2 <- x.all.df$eff*temp + rnorm(length(x.all.df$eff),0,2) + 10*x.all.df$time/(72 + x.all.df$time)
temp <- runif(length(x.all.df$eff),0,1)
x.all.df$Response <- 0
x.all.df$Response[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-30))/(1+exp((x.all.df$eff2-30))) > temp] <- 1
x.all.df$Severity <- 3
x.all.df$Severity[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-18))/(1+exp((x.all.df$eff2-18))) > temp] <- 2
x.all.df$Severity[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-28))/(1+exp((x.all.df$eff2-28))) > temp] <- 1
x.all.df$Severity_label <- plyr::mapvalues(x.all.df$Severity,c(1,2,3),c("mild","moderate","severe"))
x.all.df$Severity_label <- factor(x.all.df$Severity_label, levels = unique(x.all.df$Severity_label[order(x.all.df$Severity)]))
x.all.df$Count <- NA
x.all.df$Count[!is.na(x.all.df$eff2)]<-rpois(length(na.omit(x.all.df$eff2)), na.omit(10*( 0.5/(1 + x.all.df$time/24) + 0.5*exp(-((x.all.df$eff2)-28))/(1+exp(-((x.all.df$eff2)-28))))) )
my.data <- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT_label <- "Dosing"
my.data$CMT <- 1
temp <- x.all.df[is.na(x.all.df$amt),]
temp$CMT <- 2
temp$CMT_label <- "PK Concentration"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV <- temp$eff2
temp$CMT <- 3
temp$CMT_label <- "PD - Continuous"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,23.9+seq(1,9)*24),]
temp$DV <- temp$Count
temp$CMT <- 4
temp$CMT_label <- "PD - Count"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-24,23.9,23.9+seq(1,9)*24),]
temp$DV <- temp$Severity
temp$CMT <- 5
temp$CMT_label <- "PD - Ordinal"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV <- temp$Response
temp$CMT <- 6
temp$CMT_label <- "PD - Binary"
my.data <- rbind(my.data,temp)
my.data$DV <- signif(my.data$DV,3)
my.data$WT0 <- signif(my.data$WT0,3)
my.data$time <- round(my.data$time,3)
my.data <- my.data[order(my.data$ID,my.data$time, my.data$CMT),]
my.data$DOSE_label <- paste(my.data$DOSE,"mg")
my.data$DOSE_label[my.data$DOSE==0] <- "Placebo"
my.data$DOSE_label <- factor(my.data$DOSE_label,levels = c("Placebo",paste(unique(my.data$DOSE[my.data$DOSE!=0]),"mg")))
my.data$DAY <- floor(my.data$NT/24)+1
my.data$DAY_label <- paste("Day",ceiling(my.data$DAY))
my.data$DAY_label[my.data$DAY<=0]<-"Baseline"
my.data$DAY_label <- factor(my.data$DAY_label,
levels = c("Baseline",paste("Day",sort(unique(ceiling(my.data$DAY))))))
my.data$CYCLE <- my.data$DAY
my.data$CYCLE[my.data$CYCLE>6] <- 6
# temp <- my.data[my.data$DAY_label=="Baseline"&my.data$CMT!=2,]
# temp$Severity0 <- temp$Severity
# temp$Response0 <- temp$Response
# temp$Count0 <- temp$Count
# temp$eff20 <- temp$eff2
#
# cnames <- c("ID","Severity0","Response0","Count0","eff20")
#
# my.data2 <- merge(my.data, unique(temp[,cnames]),by=c("ID"),all.x=TRUE)
Multiple_Ascending_Dose_Dataset <- my.data[,c("ID","time","NT","amt","DV","CMT","CMT_label","BLQ","evid","WT0","SEX","DOSE_label","DOSE","DAY","DAY_label","Response","Severity","Severity_label","Count","CYCLE")]
write.csv(Multiple_Ascending_Dose_Dataset,"../Data/Multiple_Ascending_Dose_Dataset.csv",row.names = FALSE)
# DT::datatable(Multiple_Ascending_Dose_Dataset[,c("time","NT","ID","CMT","DV","DOSE","DOSE_label","WT0","SEX","DAY","DAY_label","CMT_label")], rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE))
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset
Multiple_Ascending_Dose_Dataset2$PROFTIME <- Multiple_Ascending_Dose_Dataset2$NT - (Multiple_Ascending_Dose_Dataset2$CYCLE-1)*24
Multiple_Ascending_Dose_Dataset2$NOMTIME <- Multiple_Ascending_Dose_Dataset2$NT
Multiple_Ascending_Dose_Dataset2$YTYPE <- Multiple_Ascending_Dose_Dataset2$CMT
Multiple_Ascending_Dose_Dataset2$amt[is.na(Multiple_Ascending_Dose_Dataset2$amt)] <- 0
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset2[!(Multiple_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Multiple_Ascending_Dose_Dataset2$CMT==2),]
Multiple_Ascending_Dose_Dataset2$BLQ[Multiple_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Multiple_Ascending_Dose_Dataset2$CMT==2] <- 1L
Multiple_Ascending_Dose_Dataset2$evid <- plyr::mapvalues(Multiple_Ascending_Dose_Dataset2$evid, c(NA,101), c(0,1))</code></pre>
<pre><code>## The following `from` values were not present in `x`: 101</code></pre>
<pre class="r"><code>Multiple_Ascending_Dose_Dataset2$TIMEUNIT <- "Hours"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==1] <- "mg"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==2] <- "ng/mL"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==3] <- "IU/L"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==4] <- "count"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==5] <- "severity"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==6] <- "response"
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset2[,c("ID","time","NOMTIME","TIMEUNIT","amt","DV","CMT",
"CMT_label","EVENTU","BLQ","evid","WT0","SEX","DOSE_label",
"DOSE","DAY","PROFTIME","CYCLE")]
names(Multiple_Ascending_Dose_Dataset2) <- c("ID","TIME","NOMTIME","TIMEUNIT","AMT","LIDV","CMT",
"NAME","EVENTU","CENS","EVID","WEIGHTB","SEX","TRTACT",
"DOSE","PROFDAY","PROFTIME","CYCLE")
write.csv(Multiple_Ascending_Dose_Dataset2,"../Data/Multiple_Ascending_Dose_Dataset2.csv",row.names = FALSE)</code></pre>
<pre class="r"><code>DT::datatable(Multiple_Ascending_Dose_Dataset2, rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE) )</code></pre>
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class=\"display\">\n <thead>\n <tr>\n <th>ID<\/th>\n <th>TIME<\/th>\n <th>NOMTIME<\/th>\n <th>TIMEUNIT<\/th>\n <th>AMT<\/th>\n <th>LIDV<\/th>\n <th>CMT<\/th>\n <th>NAME<\/th>\n <th>EVENTU<\/th>\n <th>CENS<\/th>\n <th>EVID<\/th>\n <th>WEIGHTB<\/th>\n <th>SEX<\/th>\n <th>TRTACT<\/th>\n <th>DOSE<\/th>\n <th>PROFDAY<\/th>\n <th>PROFTIME<\/th>\n <th>CYCLE<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":20,"autoWidth":true,"scrollX":true,"columnDefs":[{"className":"dt-right","targets":[0,1,2,4,5,6,9,10,11,14,15,16,17]}],"order":[],"orderClasses":false,"lengthMenu":[10,20,25,50,100]}},"evals":[],"jsHooks":[]}</script>
</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] RxODE_1.1.1 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
## [10] 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 gridExtra_2.3 tidyr_1.2.1
## [19] dplyr_1.0.10 ggplot2_3.3.6
##
## loaded via a namespace (and not attached):
## [1] readxl_1.4.1 backports_1.4.1 Hmisc_4.7-0 plyr_1.8.7 splines_4.1.0 RApiSerialize_0.1.0 crosstalk_1.2.0
## [8] digest_0.6.30 htmltools_0.5.3 fansi_1.0.3 magrittr_2.0.3 checkmate_2.1.0 memoise_2.0.1 googlesheets4_1.0.1
## [15] cluster_2.1.3 tzdb_0.3.0 openxlsx_4.2.4 modelr_0.1.9 RcppParallel_5.1.5 prettyunits_1.1.1 jpeg_0.1-9
## [22] colorspace_2.0-3 rvest_1.0.3 PreciseSums_0.5 haven_2.5.1 xfun_0.34 crayon_1.5.2 RCurl_1.98-1.4
## [29] jsonlite_1.8.3 Exact_2.1 stringfish_0.15.7 glue_1.6.2 gtable_0.3.1 gargle_1.2.1 car_3.0-11
## [36] abind_1.4-5 scales_1.2.1 mvtnorm_1.1-3 DBI_1.1.3 GGally_2.1.2 rstatix_0.7.0 Rcpp_1.0.9
## [43] xtable_1.8-4 progress_1.2.2 htmlTable_2.2.1 foreign_0.8-82 proxy_0.4-26 km.ci_0.5-2 Formula_1.2-4
## [50] dparser_1.3.1-5 htmlwidgets_1.5.4 httr_1.4.4 RColorBrewer_1.1-3 ellipsis_0.3.2 pkgconfig_2.0.3 reshape_0.8.8
## [57] lotri_0.4.2 farver_2.1.1 nnet_7.3-17 sass_0.4.2 dbplyr_2.2.1 binom_1.1-1 deldir_1.0-6
## [64] utf8_1.2.2 tidyselect_1.2.0 labeling_0.4.2 rlang_1.0.6 munsell_0.5.0 cellranger_1.1.0 tools_4.1.0
## [71] cachem_1.0.6 cli_3.4.1 generics_0.1.3 evaluate_0.17 fastmap_1.1.0 sys_3.4 yaml_2.3.6
## [78] fs_1.5.2 zip_2.2.0 pander_0.6.4 survMisc_0.5.5 rootSolve_1.8.2.2 nlme_3.1-160 xml2_1.3.3
## [85] compiler_4.1.0 rstudioapi_0.14 curl_4.3.3 png_0.1-7 e1071_1.7-8 ggsignif_0.6.2 reprex_2.0.2
## [92] bslib_0.4.0 DescTools_0.99.42 stringi_1.7.8 highr_0.9 lattice_0.20-45 Matrix_1.5-1 markdown_1.2
## [99] KMsurv_0.1-5 vctrs_0.5.0 pillar_1.8.1 lifecycle_1.0.3 jquerylib_0.1.4 data.table_1.14.2 bitops_1.0-7
## [106] lmom_2.8 R6_2.5.1 latticeExtra_0.6-30 qs_0.25.3 rio_0.5.27 gld_2.6.2 codetools_0.2-18
## [113] boot_1.3-28 MASS_7.3-58.1 assertthat_0.2.1 minpack.lm_1.2-1 withr_2.5.0 Deriv_4.1.3 mgcv_1.8-41
## [120] expm_0.999-6 hms_1.1.2 grid_4.1.0 rpart_4.1.16 class_7.3-19 rmarkdown_2.17 carData_3.0-4
## [127] googledrive_2.0.0 lubridate_1.8.0 base64enc_0.1-3 interp_1.1-2</code></pre>
</div>
</div>
</div>
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function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
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