simulate,DualDesign-method | R Documentation |
Simulate outcomes from a dual-endpoint design
## S4 method for signature '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(),
...
)
object |
the |
nsim |
the number of simulations (default: 1) |
seed |
see |
trueTox |
a function which takes as input a dose (vector) and returns the
true probability (vector) for toxicity. Additional arguments can be supplied
in |
trueBiomarker |
a function which takes as input a dose (vector) and
returns the true biomarker level (vector). Additional arguments can be
supplied in |
args |
data frame with arguments for the |
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 |
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 |
an object of class DualSimulations
# 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
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