simulate-DualResponsesSamplesDesign-method: Simulate dose escalation procedure using DLE and efficacy...

In crmPack: Object-Oriented Implementation of Dose Escalation Designs

Simulate dose escalation procedure using DLE and efficacy responses with samples

Description

This is a method to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the DualResponsesSamplesDesign where DLE model used are of ModelTox class object and efficacy model used are of ModelEff class object (special case is EffFlexi class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process.

Usage

## S4 method for signature 'DualResponsesSamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  trueDLE,
  trueEff,
  trueNu = NULL,
  trueSigma2 = NULL,
  trueSigma2betaW = NULL,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object	the DualResponsesSamplesDesign object we want to simulate the data from
nsim	(count) the number of simulations (default: 1)
seed	see set_seed()
trueDLE	(function) a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in args.
trueEff	(function) a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in args.
trueNu	(number) (not with EffFlexi) the precision, the inverse of the variance of the efficacy responses
trueSigma2	(number) (only with EffFlexi) the true variance of the efficacy responses which must be a single positive scalar.
trueSigma2betaW	(number) (only with EffFlexi) the true variance for the random walk model used for smoothing. This must be a single positive scalar.
args	(data.frame) data frame with arguments for the trueDLE and trueEff 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.
firstSeparate	(flag) 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	(McmcOptions) object of class McmcOptions, giving the MCMC options for each evaluation in the trial. By default, the standard options are used
parallel	(flag) should the simulation runs be parallelized across the clusters of the computer? (not default)
nCores	(count) 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 PseudoDualSimulations or PseudoDualFlexiSimulations

Examples

# nolint start

## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
data <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE)
## First for the DLE model
## The DLE model must be of 'ModelTox' (e.g 'LogisticIndepBeta') class
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## The efficacy model of 'ModelEff' (e.g 'Effloglog') class
Effmodel <- Effloglog(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data
)


## The escalation rule using the 'NextBestMaxGainSamples' class
mynextbest <- NextBestMaxGainSamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, prob = 0.3))
  },
  mg_derive = function(mg_samples) {
    as.numeric(quantile(mg_samples, prob = 0.5))
  }
)

## The increments (see Increments class examples)
## 200% allowable increase for dose below 300 and 200% increase for dose above 300
myIncrements <- IncrementsRelative(
  intervals = c(25, 300),
  increments = c(2, 2)
)
## cohort size of 3
mySize <- CohortSizeConst(size = 3)
## Stop only when 10 subjects are treated (only for illustration such a low
## sample size)
myStopping <- StoppingMinPatients(nPatients = 10)
## Now specified the design with all the above information and starting with
## a dose of 25

## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE and efficacy curve
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)
myTruthEff <- efficacyFunction(Effmodel, theta1 = -4.818429, theta2 = 3.653058)

## The true gain curve can also be seen
myTruthGain <- function(dose) {
  return((myTruthEff(dose)) / (1 + (myTruthDLE(dose) / (1 - myTruthDLE(dose)))))
}

## simulate the trial for 10 times involving samples
## for illustration purpose we use 10 burn-ins to generate 50 samples
options <- McmcOptions(burnin = 10, step = 1, samples = 50)
## For illustration purpose only 1 simulations are produced (nsim=1).
mySim <- simulate(
  design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueNu = 1 / 0.025,
  nsim = 1,
  mcmcOptions = options,
  seed = 819,
  parallel = FALSE
)


## Simulate dose-escalation procedure based on DLE and efficacy responses where DLE
## and efficacy samples are used
## when the efficacy model is of 'EffFlexi' class
Effmodel <- EffFlexi(
  eff = c(1.223, 2.513),
  eff_dose = c(25, 300),
  sigma2W = c(a = 0.1, b = 0.1),
  sigma2betaW = c(a = 20, b = 50),
  rw1 = FALSE,
  data = data
)


## Specified the design
design <- DualResponsesSamplesDesign(
  nextBest = mynextbest,
  cohort_size = mySize,
  startingDose = 25,
  model = DLEmodel,
  eff_model = Effmodel,
  data = data,
  stopping = myStopping,
  increments = myIncrements
)
## specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499)

myTruthEff <- c(
  -0.5478867,
  0.1645417,
  0.5248031,
  0.7604467,
  0.9333009,
  1.0687031,
  1.1793942,
  1.2726408,
  1.3529598,
  1.4233411,
  1.4858613,
  1.5420182
)
## The true gain curve can also be seen
d1 <- data@doseGrid
myTruthGain <- (myTruthEff) / (1 + (myTruthDLE(d1) / (1 - myTruthDLE(d1))))


mySim <- simulate(
  object = design,
  args = NULL,
  trueDLE = myTruthDLE,
  trueEff = myTruthEff,
  trueSigma2 = 0.025,
  trueSigma2betaW = 1,
  mcmcOptions = options,
  nsim = 1,
  seed = 819,
  parallel = FALSE
)

# nolint end

crmPack documentation built on Nov. 29, 2025, 5:07 p.m.