# nolint start
# Create the data
data <- Data(x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10, 20, 20, 20, 40, 40, 40,
80, 80, 80),
y=c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
cohort=c(0, 1, 2, 3, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8),
doseGrid=
c(0.1, 0.5, 1.5, 3, 6,
seq(from=10, to=80, by=2)))
# Initialize the CRM model used to model the data
model <- LogisticLogNormal(mean=c(-0.85, 1),
cov=
matrix(c(1, -0.5, -0.5, 1),
nrow=2),
ref_dose=56)
# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
step=2,
samples=2000)
set.seed(94)
samples <- mcmc(data, model, options)
# Define the rule for dose increments and calculate the maximum dose allowed
myIncrements <- IncrementsRelative(intervals=c(0, 20),
increments=c(1, 0.33))
nextMaxDose <- maxDose(myIncrements,
data=data)
# Define the rule which will be used to select the next best dose
# based on the class 'NextBestNCRM'
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
overdose=c(0.35, 1),
maxOverdoseProb=0.25)
# Calculate the next best dose
doseRecommendation <- nextBest(myNextBest,
doselimit=nextMaxDose,
samples=samples, model=model, data=data)
# Define the stopping rule such that the study would be stopped if there is at least
# 0.5 posterior probability that [0.2 =< Prob(DLT | next-best-dose) <= 0.35]
stopTarget <- StoppingTargetProb(target=c(0.2, 0.35),
prob=0.5)
## now use the StoppingHighestDose rule:
stopHigh <-
StoppingHighestDose() &
StoppingPatientsNearDose(nPatients=3, percentage=0) &
StoppingTargetProb(target=c(0, 0.2),
prob=0.5)
## and combine everything:
myStopping <- stopTarget | stopHigh
# Then evaluate if to stop the trial
stopTrial(stopping=myStopping,
dose=doseRecommendation$value,
samples=samples,
model=model,
data=data)
# nolint end
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