# 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
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