examples/Rules-method-stopTrial-StoppingHighestDose.R

In Roche/crmPack: Object-Oriented Implementation of CRM Designs

# 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),
                           max_overdose_prob=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