Simulate outcomes from a dual-endpoint design

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Description

Simulate outcomes from a dual-endpoint design

Usage

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

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)

...

not used

Value

an object of class DualSimulations

Examples

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# 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),
                        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(40)

# 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
mySims <- simulate(design,
                   trueTox=trueTox,
                   trueBiomarker=trueBiomarker,
                   sigma2W=0.01,
                   rho=0,
                   nsim=1,
                   parallel=FALSE,
                   seed=3,
                   startingDose=6,
                   mcmcOptions =
                     McmcOptions(burnin=1000,
                                 step=1,
                                 samples=3000))
  

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