# nolint start
##define the stopping rules based on the 'StoppingTDCIRatio' class
##Using only DLE responses with samples
## we need a data object with doses >= 1:
data<-Data(x=c(25,50,50,75,150,200,225,300),
y=c(0,0,0,0,1,1,1,1),
doseGrid=seq(from=25,to=300,by=25))
##model can be specified of 'Model' or 'ModelTox' class
##For example, the 'logisticIndepBeta' class model
model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data)
##define MCMC options
##for illustration purpose we use 10 burn-in and generate 50 samples
options<-McmcOptions(burnin=10,step=2,samples=50)
##samples of 'Samples' class
samples<-mcmc(data,model,options)
##define the 'StoppingTDCIRatio' class
myStopping <- StoppingTDCIRatio(target_ratio = 5, prob_target = 0.3)
##Find the next Recommend dose using the nextBest method (plesae refer to nextbest examples)
tdNextBest <- NextBestTDsamples(
prob_target_drt = 0.35, prob_target_eot = 0.3,
derive = function(samples) {
as.numeric(quantile(samples, probs = 0.3))
}
)
RecommendDose<-nextBest(tdNextBest,doselimit=max(data@doseGrid),samples=samples,
model=model,data=data)
##use 'stopTrial' to determine if the rule has been fulfilled
##use 0.3 as the target proability of DLE at the end of the trial
stopTrial(stopping=myStopping,dose=RecommendDose$next_dose_drt,
samples=samples,model=model,data=data)
# nolint end
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