In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

knitr::opts_chunk$set(echo = TRUE)
# nolint start

Simulation setting

Here the simulation study setting is defined.

id <- 1
onset <- 3
a0 <- 2
a1 <- 3
refDose <- 56

# True dose-DLT relationship
myTruth <- function(dose){
  StandLogDose <- log(dose / refDose)
  plogis(a0 + a1 * StandLogDose)
}

# The conditional CDF of the PEM
if(onset==30) {
  onset=15
  exp_cond.cdf<-function(x){
    (pexp(42-x,1/onset,lower.tail=FALSE)-pexp(Tmax_,1/onset,lower.tail=FALSE))/pexp(Tmax_,1/onset)
  }
} else{ exp_cond.cdf<-function(x){
  1-(pexp(x,1/onset,lower.tail=FALSE)-pexp(Tmax_,1/onset,lower.tail=FALSE))/pexp(Tmax_,1/onset)
}}

Design definition

Here the the dose escalation designs are defined: in this example the TITE-CRM is used. Similarly the code can be adapted for the rolling-CRM.

library(crmPack)
Tmax_ <- 42

model <- TITELogisticLogNormal(
  mean=c(1.33,1.49),
  cov=matrix(c(1.826,0.0209,0.0209,0.0245),nrow=2),
  ref_dose=refDose
)

myIncrements <- IncrementsRelative(intervals=c(0,20),
                                   increments=c(10,3))

myNextBest <- NextBestMTD(target=0.3,
                          derive=
                            function(mtdSamples){
                              mean(mtdSamples)
                            })

myStopping <- StoppingMinPatients(nPatients=48)

mySize <- CohortSizeConst(size=3)

emptydata <- DataDA(doseGrid=seq(from=2, to=50, by=2),Tmax=Tmax_,weightMethod = "adaptive")


mysafetywindow <- SafetyWindowConst(c(7,7),7,7)

design <- DADesign(model=model,
                   increments=myIncrements,
                   nextBest=myNextBest,
                   stopping=myStopping,
                   cohortSize=mySize,
                   data=emptydata,
                   safetyWindow=mysafetywindow,
                   startingDose=8)

Simulation run

In order to obtain stable results, increase the simulation parameters appropriately (step, samples, nsim).

options <- McmcOptions(burnin=20,
                       step=1,
                       samples=50)
mySims <- simulate(design,
                   args=NULL,
                   truthTox=myTruth,
                   truthSurv=exp_cond.cdf,
                   trueTmax=42,
                   nsim=10,
                   seed=819,
                   mcmcOptions=options,
                   parallel=FALSE)

# nolint end