simulate-BayDesign-method: Simulate outcomes from a Bayer CRM design
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description Usage Arguments Value Examples

Simulate outcomes from a Bayer CRM design

## S4 method for signature 'BayDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 32),
  ...
)

`object`	the `BayDesign` 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

an object of class Simulations

# nolint start

# 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),
                           ref_dose=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 <- probFunction(model, 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]
  
# nolint end