Simulate outcomes from a dual-endpoint design

Usage

# S4 method for class '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),
  derive = list(),
  ...
)

Arguments

object: the DualDesign object we want to simulate data from
nsim: the number of simulations (default: 1)
seed: see set_seed
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.
derive: a named list of functions which derives statistics, based on the vector of posterior MTD samples. Each list element must therefore accept one and only one argument, which is a numeric vector, and return a number.
...: not used

Value

an object of class DualSimulations

Examples

# nolint start

# 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(
  mean = c(0, 1),
  cov = matrix(c(1, 0, 0, 1), nrow = 2),
  sigma2betaW = 0.01,
  sigma2W = c(a = 0.1, b = 0.1),
  use_log_dose = TRUE,
  ref_dose = 2,
  rho = c(a = 1, b = 1),
  rw1 = TRUE
)

# Choose the rule for selecting the next dose
myNextBest <- NextBestDualEndpoint(
  target = c(0.9, 1),
  overdose = c(0.35, 1),
  max_overdose_prob = 0.25
)

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

# nolint end