simulate,TDsamplesDesign-method | R Documentation |
TDsamplesDesign
where model used are of
ModelTox
class object DLE samples are also usedThis 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
## S4 method for signature 'TDsamplesDesign'
simulate(
object,
nsim = 1L,
seed = NULL,
truth,
args = NULL,
firstSeparate = FALSE,
mcmcOptions = McmcOptions(),
parallel = FALSE,
nCores = min(parallel::detectCores(), 5L),
...
)
object |
the |
nsim |
the number of simulations (default :1) |
seed |
see |
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 |
data frame with arguments for the |
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 |
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 |
an object of class PseudoSimulations
@export @keywords methods
# 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
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