simulate-DualDesign-method: Simulate outcomes from a dual-endpoint design

In crmPack: Object-Oriented Implementation of CRM Designs

Simulate outcomes from a dual-endpoint design

Description

Simulate outcomes from a dual-endpoint design

Usage

## S4 method for signature 'DualDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueTox,
  trueBiomarker,
  args = NULL,
  sigma2W,
  rho = 0,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  ...
)

Arguments

object	the DualDesign object we want to simulate data from
nsim	the number of simulations (default: 1)
seed	see setSeed
trueTox	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity. Additional arguments can be supplied in args.
trueBiomarker	a function which takes as input a dose (vector) and returns the true biomarker level (vector). Additional arguments can be supplied in args.
args	data frame with arguments for the trueTox and trueBiomarker 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.
sigma2W	variance for the biomarker measurements
rho	correlation between toxicity and biomarker measurements (default: 0)
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

Value

an object of class DualSimulations

Examples

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

# Initialize the CRM model 
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        useLogDose=TRUE,
                        refDose=2,
                        rho = c(a=1, b=1),
                        smooth="RW1")

# Choose the rule for selecting the next dose 
myNextBest <- NextBestDualEndpoint(target=c(0.9, 1),
                                   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
myStopping4 <- StoppingTargetBiomarker(target=c(0.9, 1),
                                       prob=0.5)
myStopping <- myStopping4 | StoppingMinPatients(10)

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

# Initialize the design
design <- DualDesign(model = model,
                     data = emptydata,
                     nextBest = myNextBest,
                     stopping = myStopping,
                     increments = myIncrements,
                     cohortSize = mySize,
                     startingDose = 3)
  
# define scenarios for the TRUE toxicity and efficacy profiles
betaMod <- function (dose, e0, eMax, delta1, delta2, scal)
{
  maxDens <- (delta1^delta1) * (delta2^delta2)/((delta1 + delta2)^(delta1 + delta2))
  dose <- dose/scal
  e0 + eMax/maxDens * (dose^delta1) * (1 - dose)^delta2
}

trueBiomarker <- function(dose)
{
  betaMod(dose, e0=0.2, eMax=0.6, delta1=5, delta2=5 * 0.5 / 0.5, scal=100)
}

trueTox <- function(dose)
{
  pnorm((dose-60)/10)
}

# Draw the TRUE profiles
par(mfrow=c(1, 2))
curve(trueTox(x), from=0, to=80)
curve(trueBiomarker(x), from=0, to=80)

# Run the simulation on the desired design
# We only generate 1 trial outcome here for illustration, for the actual study 
# this should be increased of course, similarly for the McmcOptions - 
# they also need to be increased.
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=100,
                                 step=1,
                                 samples=300))
  

crmPack documentation built on Sept. 3, 2022, 1:05 a.m.