This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDsamplesDesign where model used are of ModelTox class object DLE samples are also used

This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the TDsamplesDesign where model used are of ModelTox class object DLE samples are also used

Usage

# S4 method for class 'TDsamplesDesign'
simulate(
  object,
  nsim = 1L,
  seed = NULL,
  truth,
  args = NULL,
  firstSeparate = FALSE,
  mcmcOptions = McmcOptions(),
  parallel = FALSE,
  nCores = min(parallel::detectCores(), 5L),
  ...
)

Arguments

object

the TDsamplesDesign object we want to simulate the data from

nsim

the number of simulations (default :1)

seed

see set_seed

truth

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.

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.

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 PseudoSimulations

@export @keywords methods

Examples

# nolint start

## Simulate dose-escalation procedure based only on DLE responses with DLE samples involved

## The design comprises a model, the escalation rule, starting data,
## a cohort size and a starting dose
## Define your data set first using an empty data set
## with dose levels from 25 to 300 with increments 25
data <- Data(doseGrid = seq(25, 300, 25))

## The design only incorporate DLE responses and DLE samples are involved
## Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model
model <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)
## Then the escalation rule
tdNextBest <- NextBestTDsamples(
  prob_target_drt = 0.35,
  prob_target_eot = 0.3,
  derive = function(samples) {
    as.numeric(quantile(samples, probs = 0.3))
  }
)

## The cohort size, size of 3 subjects
mySize <- CohortSizeConst(size = 3)
## Deifne the increments for the dose-escalation process
## The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid
## The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid
## This is to specified a maximum of 3-fold restriction in dose-esclation
myIncrements <- IncrementsRelative(
  intervals = c(min(data@doseGrid), max(data@doseGrid)),
  increments = c(2, 2)
)
## Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached
myStopping <- StoppingMinPatients(nPatients = 36)

## Specified the design(for details please refer to the 'TDsamplesDesign' example)
design <- TDsamplesDesign(
  model = model,
  nextBest = tdNextBest,
  stopping = myStopping,
  increments = myIncrements,
  cohort_size = mySize,
  data = data, startingDose = 25
)

## Specify the truth of the DLE responses
myTruth <- probFunction(model, phi1 = -53.66584, phi2 = 10.50499)

## then plot the truth to see how the truth dose-DLE curve look like
curve(myTruth(x), from = 0, to = 300, ylim = c(0, 1))


## Then specified the simulations and generate the trial
## options for MCMC
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
## The simulations
## For illustration purpose only 1 simulation is produced (nsim=1).
mySim <- simulate(
  object = design,
  args = NULL,
  truth = myTruth,
  nsim = 1,
  seed = 819,
  mcmcOptions = options,
  parallel = FALSE
)

# nolint end