Simulate outcomes from a time-to-DLT augmented CRM design (DADesign)

Simulate outcomes from a time-to-DLT augmented CRM design (DADesign)

Usage

# S4 method for class 'DADesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truthTox,
  truthSurv,
  trueTmax = NULL,
  args = NULL,
  firstSeparate = FALSE,
  deescalate = TRUE,
  mcmcOptions = McmcOptions(),
  DA = TRUE,
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5),
  derive = list(),
  ...
)

Arguments

object: the DADesign object we want to simulate data from
nsim: the number of simulations (default: 1)
seed: see set_seed
truthTox: a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity and the time DLT occurs. Additional arguments can be supplied in args.
truthSurv: a CDF which takes as input a time (vector) and returns the true cumulative probability (vector) that the DLT would occur conditioning on the patient has DLTs.
trueTmax: (number or NULL)
the true maximum time at which DLTs can occur. Note that this must be larger thank Tmax from the object's base data, which is the length of the DLT window, i.e. until which time DLTs are officially declared as such and used in the trial.
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.
deescalate: deescalation when a DLT occurs in cohorts with lower dose level.
mcmcOptions: object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used.
DA: document or rename this parameter to make it more meaningful
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 Simulations

Examples

# nolint start

# Define the dose-grid and PEM parameters
emptydata <- DataDA(
  doseGrid = c(0.1, 0.5, 1, 1.5, 3, 6, seq(from = 10, to = 80, by = 2)),
  Tmax = 60
)

# Initialize the mDA-CRM model
npiece_ <- 10
Tmax_ <- 60

lambda_prior <- function(k) {
  npiece_ / (Tmax_ * (npiece_ - k + 0.5))
}

model <- DALogisticLogNormal(
  mean = c(-0.85, 1),
  cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
  ref_dose = 56,
  npiece = npiece_,
  l = as.numeric(t(apply(as.matrix(c(1:npiece_), 1, npiece_), 2, lambda_prior))),
  c_par = 2
)
# Choose the rule for dose increments
myIncrements <- IncrementsRelative(
  intervals = c(0, 20),
  increments = c(1, 0.33)
)

myNextBest <- NextBestNCRM(
  target = c(0.2, 0.35),
  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
myStopping1 <- StoppingTargetProb(
  target = c(0.2, 0.35),
  prob = 0.5
)
myStopping2 <- StoppingMinPatients(nPatients = 50)

myStopping <- (myStopping1 | myStopping2)

# Choose the safety window
mysafetywindow <- SafetyWindowConst(c(6, 2), 7, 7)

# Initialize the design
design <- DADesign(
  model = model,
  increments = myIncrements,
  nextBest = myNextBest,
  stopping = myStopping,
  cohort_size = mySize,
  data = emptydata,
  safetyWindow = mysafetywindow,
  startingDose = 3
)

## set up truth curves
myTruth <- probFunction(model, alpha0 = 2, alpha1 = 3)
curve(myTruth(x), from = 0, to = 100, ylim = c(0, 1))


exp_cond.cdf <- function(x, onset = 15) {
  a <- pexp(28, 1 / onset, lower.tail = FALSE)
  1 - (pexp(x, 1 / onset, lower.tail = FALSE) - a) / (1 - a)
}

# set up simulation settings
options <- McmcOptions(
  burnin = 10,
  step = 1,
  samples = 200
)

mySims <- simulate(design,
  args = NULL,
  truthTox = myTruth,
  truthSurv = exp_cond.cdf,
  trueTmax = 80,
  nsim = 2,
  seed = 819,
  mcmcOptions = options,
  firstSeparate = TRUE,
  deescalate = FALSE,
  parallel = FALSE
)

# nolint end