examples/design-method-simulateTDsamplesDesign.R

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

# nolint start

##Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

##The design comprises a model, the escalation rule, starting data,
##a cohort size and a starting dose
##Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid=seq(25,300,25))

##The design only incorporate DLE responses and DLE samples are involved
##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##Then the escalation rule
tdNextBest<-NextBestTDsamples(targetDuringTrial=0.35,
                              targetEndOfTrial=0.3,
                              derive=function(TDsamples){quantile(TDsamples,probs=0.3)})


## The cohort size, size of 3 subjects
mySize <-CohortSizeConst(size=3)
##Deifne the increments for the dose-escalation process
##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
##This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)),
                                 increments=c(2,2))
##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients=36)

##Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(model=model,
                          nextBest=tdNextBest,
                          stopping=myStopping,
                          increments=myIncrements,
                          cohortSize=mySize,
                          data=data,startingDose=25)

##Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

##then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from=0, to=300,ylim=c(0,1))

## Then specified the simulations and generate the trial
##options for MCMC
options<-McmcOptions(burnin=100,step=2,samples=200)
##The simulations
##For illustration purpose only 1 simulation is produced (nsim=1).
mySim <-  simulate(object=design,
                   args=NULL,
                   truth=myTruth,
                   nsim=1,
                   seed=819,
                   mcmcOptions=options,
                   parallel=FALSE)

# nolint end