PKPD_Datasets.html

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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 &lt;- &quot;
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;
&quot;

work &lt;- tempfile(&quot;Rx_intro-&quot;)
mod1 &lt;- RxODE(model = ode, modName = &quot;mod1&quot;)

cat(gsub(pattern=&quot;\nConcentration&quot;,replacement=&quot;Concentration&quot;,x=gsub(pattern=&quot;;\n&quot;,replacement=&quot; \n&quot;, 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 &lt;- 5
nsub &lt;- 10 # number of subproblems

SEX &lt;- rep(c(rep(&quot;Male&quot;,ceiling(nsub/2)),rep(&quot;Female&quot;,floor(nsub/2))),ndose)
WT0 &lt;- (SEX==&quot;Male&quot;)*runif(length(SEX),60,110)+(SEX==&quot;Female&quot;)*runif(length(SEX),50,100)
VCOV &lt;- (WT0/70)*(1-0.5*(SEX==&quot;Female&quot;))
CLCOV &lt;- (WT0/70)*(1-0.5*(SEX==&quot;Female&quot;))

CL &lt;- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA &lt;- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 &lt;- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV
DOSE &lt;- sort(rep(c(100,200,400,800,1600),nsub))
theta.all &lt;- 
  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 &lt;- c(-0.1,0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9)

DOSING &lt;- c(0)
NT &lt;- data.frame(NT = c(SAMPLING, DOSING), label = c(rep(&quot;SAMPLING&quot;,length(SAMPLING)),rep(&quot;DOSING&quot;,length(DOSING))))
NT &lt;- NT[order(NT$NT),]

ev.all &lt;- list()
for(idose in 1:length(DOSE)){
  ev.all[[idose]] &lt;- eventTable(amount.units=&#39;mg&#39;, time.units=&#39;hours&#39;)
  # 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 &amp; dosing schedules
  temp &lt;- NT
  temp$time &lt;- cumsum(c(temp$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 &lt;- temp$time - temp$time[temp$NT==0] # center at first dose
  
  ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label==&quot;DOSING&quot;])
  ev.all[[idose]]$add.sampling(temp$time[temp$label==&quot;SAMPLING&quot;])
  
}

x.all.df &lt;- NULL
ID = 0
for(i in 1:length(ev.all)){
  #   for(i in 1:nsub){
  ID = ID + 1
  theta &lt;- theta.all[i,]
  inits &lt;- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta[&quot;Kin&quot;]/theta[&quot;Kout&quot;])
  x &lt;- mod1$solve(theta, ev.all[[i]], inits = inits)
  
  x.df &lt;- data.frame(x)
  x.df$NT &lt;- SAMPLING
  
  temp &lt;- data.frame(ev.all[[i]]$get.dosing())
  temp$NT &lt;- DOSING
  
  temp[,setdiff(names(x.df),names(temp))]&lt;- NA
  x.df[,setdiff(names(temp),names(x.df))]&lt;- NA
  x.df &lt;- rbind(x.df,temp)
  
  x.df$ID &lt;- ID
  x.df$DOSE &lt;- DOSE[i]
  x.df$WT0 &lt;- WT0[i]
  x.df$SEX &lt;- SEX[i]
  
  if(is.null(x.all.df)){
    x.all.df &lt;- x.df
  }else{
    x.all.df &lt;- 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 &#39;rxSetIni0(FALSE)&#39; to initialize at first observed time
## this warning is displayed once per session</code></pre>
<pre class="r"><code>x.all.df$DV &lt;- x.all.df$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&lt;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 &lt;- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT &lt;- 1
my.data$CMT_label &lt;- &quot;Dosing&quot;

temp &lt;- x.all.df[is.na(x.all.df$amt),]
temp$CMT &lt;- 2
temp$CMT_label &lt;- &quot;PK Concentration&quot;
my.data &lt;- rbind(my.data,temp)

my.data$DV &lt;- signif(my.data$DV,3)
my.data$WT0 &lt;- signif(my.data$WT0,3)
my.data$time &lt;- round(my.data$time,3)

my.data &lt;- my.data[order(my.data$ID,my.data$time, my.data$CMT),]

my.data$DOSE_label &lt;- paste(my.data$DOSE,&quot;mg&quot;)
my.data$DOSE_label[my.data$DOSE==0] &lt;- &quot;Placebo&quot;
my.data$DOSE_label &lt;- factor(my.data$DOSE_label,levels = c(&quot;Placebo&quot;,paste(unique(my.data$DOSE[my.data$DOSE!=0]),&quot;mg&quot;)))

Single_Ascending_Dose_Dataset &lt;- my.data[,c(&quot;ID&quot;,&quot;time&quot;,&quot;NT&quot;,&quot;amt&quot;,&quot;DV&quot;,&quot;CMT&quot;,&quot;CMT_label&quot;,&quot;BLQ&quot;,&quot;evid&quot;,&quot;WT0&quot;,&quot;SEX&quot;,&quot;DOSE&quot;,&quot;DOSE_label&quot;)]
write.csv(Single_Ascending_Dose_Dataset,&quot;../Data/Single_Ascending_Dose_Dataset.csv&quot;,row.names = FALSE)

Single_Ascending_Dose_Dataset2 &lt;- Single_Ascending_Dose_Dataset
Single_Ascending_Dose_Dataset2$PROFTIME &lt;- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$NOMTIME &lt;- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$YTYPE &lt;- Single_Ascending_Dose_Dataset2$CMT
Single_Ascending_Dose_Dataset2$amt[is.na(Single_Ascending_Dose_Dataset2$amt)] &lt;- 0 
Single_Ascending_Dose_Dataset2 &lt;- Single_Ascending_Dose_Dataset2[!(Single_Ascending_Dose_Dataset2$DOSE_label==&quot;Placebo&quot;&amp;
                                      Single_Ascending_Dose_Dataset2$CMT==2),] 
Single_Ascending_Dose_Dataset2$BLQ[Single_Ascending_Dose_Dataset2$DOSE_label==&quot;Placebo&quot;&amp;
                                       Single_Ascending_Dose_Dataset2$CMT==2] &lt;- 1L
Single_Ascending_Dose_Dataset2$evid &lt;- 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 &lt;- &quot;Hours&quot;
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==1] &lt;- &quot;mg&quot;
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==2] &lt;- &quot;ng/mL&quot;

Single_Ascending_Dose_Dataset2 &lt;- Single_Ascending_Dose_Dataset2[,c(&quot;ID&quot;,&quot;time&quot;,&quot;NOMTIME&quot;,&quot;TIMEUNIT&quot;,&quot;amt&quot;,&quot;DV&quot;,&quot;CMT&quot;,
                                                                        &quot;CMT_label&quot;,&quot;EVENTU&quot;,&quot;BLQ&quot;,&quot;evid&quot;,&quot;WT0&quot;,&quot;SEX&quot;,&quot;DOSE_label&quot;,
                                                                        &quot;DOSE&quot;)]
names(Single_Ascending_Dose_Dataset2) &lt;- c(&quot;ID&quot;,&quot;TIME&quot;,&quot;NOMTIME&quot;,&quot;TIMEUNIT&quot;,&quot;AMT&quot;,&quot;LIDV&quot;,&quot;CMT&quot;,
                                             &quot;NAME&quot;,&quot;EVENTU&quot;,&quot;CENS&quot;,&quot;EVID&quot;,&quot;WEIGHTB&quot;,&quot;SEX&quot;,&quot;TRTACT&quot;,
                                             &quot;DOSE&quot;)

write.csv(Single_Ascending_Dose_Dataset2,&quot;../Data/Single_Ascending_Dose_Dataset2.csv&quot;,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>
<div id="htmlwidget-ca9d2105634049c29be6" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" 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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>
</div>
<div id="multiple-ascending-dose-pk-pd-dataset" class="section level3">
<h3>Multiple Ascending Dose PK &amp; PD Dataset</h3>
<pre class="r"><code>set.seed(12345666)
ndose &lt;- 6
nsub &lt;- 10 # number of subproblems

SEX &lt;- rep(c(rep(&quot;Male&quot;,ceiling(nsub/2)),rep(&quot;Female&quot;,floor(nsub/2))),ndose)
WT0 &lt;- (SEX==&quot;Male&quot;)*runif(length(SEX),60,110)+(SEX==&quot;Female&quot;)*runif(length(SEX),50,100)
VCOV &lt;- (WT0/70)*(1-0.5*(SEX==&quot;Female&quot;))
CLCOV &lt;- (WT0/70)*(1-0.5*(SEX==&quot;Female&quot;))

CL &lt;- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA &lt;- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 &lt;- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV

Kin &lt;- 0.1*exp(rnorm(nsub*ndose,0,0.4^2))
DOSE &lt;- sort(rep(c(0,100,200,400,800,1600),nsub))
theta.all &lt;- 
  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==&quot;Female&quot;))

# Nominal times for sampling schedule
SAMPLING &lt;- 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 &lt;- seq(0,5*24,24)
NT &lt;- data.frame(NT = c(SAMPLING, DOSING), label = c(rep(&quot;SAMPLING&quot;,length(SAMPLING)),rep(&quot;DOSING&quot;,length(DOSING))))
NT &lt;- NT[order(NT$NT),]

ev.all &lt;- list()
for(idose in 1:length(DOSE)){
  ev.all[[idose]] &lt;- eventTable(amount.units=&#39;mg&#39;, time.units=&#39;hours&#39;)
  # 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 &amp; dosing schedules
  temp &lt;- NT
  temp$time &lt;- cumsum(c(temp$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 &lt;- temp$time - temp$time[temp$NT==0] # center at first dose
  
  if(DOSE[idose] &gt; 0){
      ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label==&quot;DOSING&quot;])
  }else{
      ev.all[[idose]]$add.dosing(dose = 1e-12, start.time = temp$time[temp$label==&quot;DOSING&quot;])
  }

  ev.all[[idose]]$add.sampling(temp$time[temp$label==&quot;SAMPLING&quot;])
  
}

x.all.df &lt;- NULL
ID = 0
for(i in 1:length(ev.all)){
  ID = ID + 1
  theta &lt;- theta.all[i,]
  inits &lt;- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta[&quot;Kin&quot;]/theta[&quot;Kout&quot;])
  x &lt;- mod1$solve(theta, ev.all[[i]], inits = inits)
  
  x.df &lt;- data.frame(x)
  x.df$NT &lt;- SAMPLING
    
  temp &lt;- data.frame(ev.all[[i]]$get.dosing())
  temp$NT &lt;- DOSING
  temp[,setdiff(names(x.df),names(temp))]&lt;- NA
  x.df[,setdiff(names(temp),names(x.df))]&lt;- NA
  x.df &lt;- rbind(x.df,temp)
  
  x.df$ID &lt;- ID
  x.df$DOSE &lt;- DOSE[i]
  x.df$WT0 &lt;- WT0[i]
  x.df$SEX &lt;- SEX[i]
  
  if(is.null(x.all.df)){
    x.all.df &lt;- x.df
  }else{
    x.all.df &lt;- rbind(x.all.df, x.df)
  }
}
x.all.df$DV &lt;- x.all.df$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&lt;0.05]=0.05
x.all.df$DV[x.all.df$Concentration==0]=NA
x.all.df$BLQ &lt;- 0
x.all.df$BLQ[x.all.df$DV==0.05] &lt;- 1

temp &lt;- exp(rnorm(length(x.all.df$eff),0,0.4^2))
x.all.df$eff2 &lt;- x.all.df$eff*temp + rnorm(length(x.all.df$eff),0,2) + 10*x.all.df$time/(72 + x.all.df$time)

temp &lt;- runif(length(x.all.df$eff),0,1)
x.all.df$Response &lt;- 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))) &gt; temp] &lt;- 1 


x.all.df$Severity &lt;- 3
x.all.df$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))) &gt; temp] &lt;- 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$eff2-28))) &gt; temp] &lt;- 1
x.all.df$Severity_label &lt;- plyr::mapvalues(x.all.df$Severity,c(1,2,3),c(&quot;mild&quot;,&quot;moderate&quot;,&quot;severe&quot;))
x.all.df$Severity_label &lt;- factor(x.all.df$Severity_label, levels = unique(x.all.df$Severity_label[order(x.all.df$Severity)]))

x.all.df$Count &lt;- NA
x.all.df$Count[!is.na(x.all.df$eff2)]&lt;-rpois(length(na.omit(x.all.df$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 &lt;- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT_label &lt;- &quot;Dosing&quot;
my.data$CMT &lt;- 1

temp &lt;- x.all.df[is.na(x.all.df$amt),]
temp$CMT &lt;- 2
temp$CMT_label &lt;- &quot;PK Concentration&quot;
my.data &lt;- rbind(my.data,temp)

temp &lt;- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV &lt;- temp$eff2
temp$CMT &lt;- 3
temp$CMT_label &lt;- &quot;PD - Continuous&quot;
my.data &lt;- rbind(my.data,temp)

temp &lt;- x.all.df[x.all.df$NT%in%c(-0.1,23.9,23.9+seq(1,9)*24),]
temp$DV &lt;- temp$Count
temp$CMT &lt;- 4
temp$CMT_label &lt;- &quot;PD - Count&quot;
my.data &lt;- rbind(my.data,temp)

temp &lt;- x.all.df[x.all.df$NT%in%c(-24,23.9,23.9+seq(1,9)*24),]
temp$DV &lt;- temp$Severity
temp$CMT &lt;- 5
temp$CMT_label &lt;- &quot;PD - Ordinal&quot;
my.data &lt;- rbind(my.data,temp)

temp &lt;- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV &lt;- temp$Response
temp$CMT &lt;- 6
temp$CMT_label &lt;- &quot;PD - Binary&quot;
my.data &lt;- rbind(my.data,temp)


my.data$DV &lt;- signif(my.data$DV,3)
my.data$WT0 &lt;- signif(my.data$WT0,3)
my.data$time &lt;- round(my.data$time,3)

my.data &lt;- my.data[order(my.data$ID,my.data$time, my.data$CMT),]

my.data$DOSE_label &lt;- paste(my.data$DOSE,&quot;mg&quot;)
my.data$DOSE_label[my.data$DOSE==0] &lt;- &quot;Placebo&quot;
my.data$DOSE_label &lt;- factor(my.data$DOSE_label,levels = c(&quot;Placebo&quot;,paste(unique(my.data$DOSE[my.data$DOSE!=0]),&quot;mg&quot;)))

my.data$DAY &lt;- floor(my.data$NT/24)+1
my.data$DAY_label &lt;- paste(&quot;Day&quot;,ceiling(my.data$DAY))
my.data$DAY_label[my.data$DAY&lt;=0]&lt;-&quot;Baseline&quot;
my.data$DAY_label &lt;- factor(my.data$DAY_label,
                            levels = c(&quot;Baseline&quot;,paste(&quot;Day&quot;,sort(unique(ceiling(my.data$DAY))))))
my.data$CYCLE &lt;- my.data$DAY
my.data$CYCLE[my.data$CYCLE&gt;6] &lt;- 6


# temp &lt;- my.data[my.data$DAY_label==&quot;Baseline&quot;&amp;my.data$CMT!=2,]
# temp$Severity0 &lt;- temp$Severity
# temp$Response0 &lt;- temp$Response
# temp$Count0 &lt;- temp$Count
# temp$eff20 &lt;- temp$eff2
# 
# cnames &lt;- c(&quot;ID&quot;,&quot;Severity0&quot;,&quot;Response0&quot;,&quot;Count0&quot;,&quot;eff20&quot;)
# 
# my.data2 &lt;- merge(my.data, unique(temp[,cnames]),by=c(&quot;ID&quot;),all.x=TRUE)


Multiple_Ascending_Dose_Dataset &lt;- my.data[,c(&quot;ID&quot;,&quot;time&quot;,&quot;NT&quot;,&quot;amt&quot;,&quot;DV&quot;,&quot;CMT&quot;,&quot;CMT_label&quot;,&quot;BLQ&quot;,&quot;evid&quot;,&quot;WT0&quot;,&quot;SEX&quot;,&quot;DOSE_label&quot;,&quot;DOSE&quot;,&quot;DAY&quot;,&quot;DAY_label&quot;,&quot;Response&quot;,&quot;Severity&quot;,&quot;Severity_label&quot;,&quot;Count&quot;,&quot;CYCLE&quot;)]

write.csv(Multiple_Ascending_Dose_Dataset,&quot;../Data/Multiple_Ascending_Dose_Dataset.csv&quot;,row.names = FALSE)

# DT::datatable(Multiple_Ascending_Dose_Dataset[,c(&quot;time&quot;,&quot;NT&quot;,&quot;ID&quot;,&quot;CMT&quot;,&quot;DV&quot;,&quot;DOSE&quot;,&quot;DOSE_label&quot;,&quot;WT0&quot;,&quot;SEX&quot;,&quot;DAY&quot;,&quot;DAY_label&quot;,&quot;CMT_label&quot;)], rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE))

Multiple_Ascending_Dose_Dataset2 &lt;- Multiple_Ascending_Dose_Dataset
Multiple_Ascending_Dose_Dataset2$PROFTIME &lt;- Multiple_Ascending_Dose_Dataset2$NT - (Multiple_Ascending_Dose_Dataset2$CYCLE-1)*24
Multiple_Ascending_Dose_Dataset2$NOMTIME &lt;- Multiple_Ascending_Dose_Dataset2$NT
Multiple_Ascending_Dose_Dataset2$YTYPE &lt;- Multiple_Ascending_Dose_Dataset2$CMT
Multiple_Ascending_Dose_Dataset2$amt[is.na(Multiple_Ascending_Dose_Dataset2$amt)] &lt;- 0 
Multiple_Ascending_Dose_Dataset2 &lt;- Multiple_Ascending_Dose_Dataset2[!(Multiple_Ascending_Dose_Dataset2$DOSE_label==&quot;Placebo&quot;&amp;
                                      Multiple_Ascending_Dose_Dataset2$CMT==2),] 
Multiple_Ascending_Dose_Dataset2$BLQ[Multiple_Ascending_Dose_Dataset2$DOSE_label==&quot;Placebo&quot;&amp;
                                       Multiple_Ascending_Dose_Dataset2$CMT==2] &lt;- 1L
Multiple_Ascending_Dose_Dataset2$evid &lt;- plyr::mapvalues(Multiple_Ascending_Dose_Dataset2$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$TIMEUNIT &lt;- &quot;Hours&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==1] &lt;- &quot;mg&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==2] &lt;- &quot;ng/mL&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==3] &lt;- &quot;IU/L&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==4] &lt;- &quot;count&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==5] &lt;- &quot;severity&quot;
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==6] &lt;- &quot;response&quot;

Multiple_Ascending_Dose_Dataset2 &lt;- Multiple_Ascending_Dose_Dataset2[,c(&quot;ID&quot;,&quot;time&quot;,&quot;NOMTIME&quot;,&quot;TIMEUNIT&quot;,&quot;amt&quot;,&quot;DV&quot;,&quot;CMT&quot;,
                                                                        &quot;CMT_label&quot;,&quot;EVENTU&quot;,&quot;BLQ&quot;,&quot;evid&quot;,&quot;WT0&quot;,&quot;SEX&quot;,&quot;DOSE_label&quot;,
                                                                        &quot;DOSE&quot;,&quot;DAY&quot;,&quot;PROFTIME&quot;,&quot;CYCLE&quot;)]
names(Multiple_Ascending_Dose_Dataset2) &lt;- c(&quot;ID&quot;,&quot;TIME&quot;,&quot;NOMTIME&quot;,&quot;TIMEUNIT&quot;,&quot;AMT&quot;,&quot;LIDV&quot;,&quot;CMT&quot;,
                                             &quot;NAME&quot;,&quot;EVENTU&quot;,&quot;CENS&quot;,&quot;EVID&quot;,&quot;WEIGHTB&quot;,&quot;SEX&quot;,&quot;TRTACT&quot;,
                                             &quot;DOSE&quot;,&quot;PROFDAY&quot;,&quot;PROFTIME&quot;,&quot;CYCLE&quot;)

write.csv(Multiple_Ascending_Dose_Dataset2,&quot;../Data/Multiple_Ascending_Dose_Dataset2.csv&quot;,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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6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,4,4,4,4,4,4,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6]],"container":"<table 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>
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