simulate-DADesign-method: Simulate outcomes from a time-to-DLT augmented CRM design...

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

Description

Simulate outcomes from a time-to-DLT augmented CRM design (DADesign)

Usage

## S4 method for signature 'DADesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truthTox,
  truthSurv,
  trueTmax = NULL,
  args = NULL,
  firstSeparate = FALSE,
  deescalate = TRUE,
  mcmcOptions = McmcOptions(),
  DA = TRUE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

object	the DADesign object we want to simulate data from
nsim	the number of simulations (default: 1)
seed	see setSeed
truthTox	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity and the time DLT occurs. Additional arguments can be supplied in args.
truthSurv	a CDF which takes as input a time (vector) and returns the true cumulative probability (vector) that the DLT would occur conditioning on the patient has DLTs.
trueTmax	add documentation here
args	data frame with arguments for the truth function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the nsim simulations. In order to produce outcomes from the posterior predictive distribution, e.g, pass an object that contains the data observed so far, truth contains the prob function from the model in object, and args contains posterior samples from the model.
firstSeparate	enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient.
deescalate	deescalation when a DLT occurs in cohorts with lower dose level.
mcmcOptions	object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used.
DA	document or rename this parameter to make it more meaningful
parallel	should the simulation runs be parallelized across the clusters of the computer? (not default)
nCores	how many cores should be used for parallel computing? Defaults to the number of cores on the machine (maximum 5)
...	not used

Value

an object of class Simulations

Examples

# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(doseGrid=c(0.1, 0.5,1, 1.5, 3, 6,
                               seq(from=10, to=80, by=2)),Tmax=60)
# Initialize the mDA-CRM model 
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)
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0,20),
                                   increments=c(1,0.33))
# Choose the rule for selecting the next dose 
nextMaxDose <- maxDose(myIncrements,data=emptydata)

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

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping2 <- StoppingMinPatients(nPatients=50)

myStopping <- (myStopping1 | myStopping2)

# Choose the safety window 
mysafetywindow=SafetyWindowConst(c(6,2),7,7)

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

## set up truth 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)
}

#set up simulation settings
options <- McmcOptions(burnin=100,
                       step=1,
                       samples=2000)

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=TRUE,
                   nCores=2)

# nolint end

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