crmPack (version 1.0.6)

simulate,Design-method: Simulate outcomes from a CRM design

Description

Simulate outcomes from a CRM design

Usage

# S4 method for Design
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Value

an object of class Simulations

Arguments

object

the Design object we want to simulate data from

nsim

the number of simulations (default: 1)

seed

see setSeed

truth

a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.

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.

mcmcOptions

object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used

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

Examples

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
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 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

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

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=1,
                       samples=2000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]