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

knitr::opts_chunk$set(echo = TRUE)
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Example 1: Recommend a dose for the next cohort

Setting up the data

library(crmPack)
data <- DataDA(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10), 
               y=c(0, 0, 1, 1, 0, 0, 1, 0),
               doseGrid=
                 c(0.1, 0.5, 1.5, 3, 6,
                   seq(from=10, to=80, by=2)),
               u=c(42,30,15,5,20,25,30,60),
               t0=rep(0, 8),
               Tmax=60)

emptydata <- DataDA(doseGrid=c(0.1, 0.5,1, 1.5, 3, 6,
                               seq(from=10, to=80, by=2)),
                    Tmax=60)

Structure of the model class

npiece_=10
Tmax_=60

lambda_prior<-function(k){
  npiece_/(Tmax_*(npiece_-k+0.5))
}

model<-DALogisticLogNormal(mean=c(-0.85,1),
                           cov=matrix(c(1,-0.5,-0.5,1),nrow=2),
                           ref_dose=56,
                           npiece=npiece_,
                           l=as.numeric(t(apply(as.matrix(c(1:npiece_),1,npiece_),2,lambda_prior))),
                           C_par=2)

Obtain the posterior

options <- McmcOptions(burnin=10,
                       step=2,
                       samples=1e2)

set.seed(94)
samples <- mcmc(data, model, options)

Use ggmcmc to diagnose

library(ggmcmc)
alpha0samples <- get(samples,"alpha0")

print(ggs_traceplot(alpha0samples))

print(ggs_autocorrelation(alpha0samples))

Plot the model fit

plot(samples, model,data,hazard=TRUE)

plot(samples, model,data,hazard=FALSE)

prior mean curve

emptydata <- DataDA(doseGrid=c(0.1, 0.5, 1.5, 3, 6,
                               seq(from=10, to=80, by=2)),Tmax=60)

Priorsamples <- mcmc(emptydata, model, options)

plot(Priorsamples, model,emptydata,hazard=FALSE)

Escalation rules

Need to fill in (use the same rule in the section 8 of "using the package crmPack: introductory examples")

myIncrements <- IncrementsRelative(intervals=c(0,20),
                                   increments=c(1,0.33))

nextMaxDose <- maxDose(myIncrements,data=data)

myNextBest <- NextBestNCRM(target=c(0.2,0.35),
                           overdose=c(0.35,1),
                           maxOverdoseProb=0.25)

mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

myStopping1 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping2 <- StoppingMinPatients(nPatients=50)

myStopping <- (myStopping1 | myStopping2)

Recommended dose for the next cohort

doseRecommendation <- nextBest(myNextBest,
                               doselimit=nextMaxDose,
                               samples=samples,
                               model=model,
                               data=data)


doseRecommendation$plot
doseRecommendation$value

Example 2: Run a simulation to evaluate operating characteristics

Set up safety window and DADesign to be completed

mysafetywindow <- SafetyWindowConst(c(6,2),7,7)

design <- DADesign(model=model,
                   increments=myIncrements,
                   nextBest=myNextBest,
                   stopping=myStopping,
                   cohortSize=mySize,
                   data=emptydata,
                   safetyWindow=mysafetywindow,
                   startingDose=3)

Set up true curves

myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
curve(myTruth(x), from=0, to=100, ylim=c(0, 1))

onset <- 15
exp_cond.cdf<-function(x){

  1-(pexp(x,1/onset,lower.tail=FALSE)-pexp(28,1/onset,lower.tail=FALSE))/pexp(28,1/onset)

}

Perform the simulations

mySims <- simulate(design,
                   args=NULL,
                   truthTox=myTruth,
                   truthSurv=exp_cond.cdf,#piece_exp_cond.cdf,
                   trueTmax=80,
                   nsim=2,
                   seed=819,
                   mcmcOptions=options,
                   firstSeparate=TRUE,
                   deescalate=FALSE,
                   parallel=FALSE)

Interpret the simulation results

Use a similar way as section 9.2 in the "using the package crmPack: introductory examples" document

a <- summary(mySims,truth=myTruth)
b <- mySims@data[[1]]

plot(mySims)
plot(b)

mySims@stopReasons[[2]]

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0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.