summary-Simulations-method: Summarize the model-based design simulations, relative to a...

In crmPack: Object-Oriented Implementation of CRM Designs

Summarize the model-based design simulations, relative to a given truth

Description

Summarize the model-based design simulations, relative to a given truth

Usage

## S4 method for signature 'Simulations'
summary(object, truth, target = c(0.2, 0.35), ...)

Arguments

object	the Simulations object we want to summarize
truth	a function which takes as input a dose (vector) and returns the true probability (vector) for toxicity
target	the target toxicity interval (default: 20-35%) used for the computations
...	Additional arguments can be supplied here for truth

Value

an object of class SimulationsSummary

Examples

# Define the dose-grid
emptydata <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model 
model <- LogisticLogNormal(mean=c(-0.85, 1),
                           cov=
                             matrix(c(1, -0.5, -0.5, 1),
                                    nrow=2),
                           refDose=56)

# Choose the rule for selecting the next dose 
myNextBest <- NextBestNCRM(target=c(0.2, 0.35),
                           overdose=c(0.35, 1),
                           maxOverdoseProb=0.25)

# Choose the rule for the cohort-size 
mySize1 <- CohortSizeRange(intervals=c(0, 30),
                           cohortSize=c(1, 3))
mySize2 <- CohortSizeDLT(DLTintervals=c(0, 1),
                         cohortSize=c(1, 3))
mySize <- maxSize(mySize1, mySize2)

# Choose the rule for stopping
myStopping1 <- StoppingMinCohorts(nCohorts=3)
myStopping2 <- StoppingTargetProb(target=c(0.2, 0.35),
                                  prob=0.5)
myStopping3 <- StoppingMinPatients(nPatients=20)
myStopping <- (myStopping1 & myStopping2) | myStopping3

# Choose the rule for dose increments
myIncrements <- IncrementsRelative(intervals=c(0, 20),
                                   increments=c(1, 0.33))

# Initialize the design
design <- Design(model=model,
                 nextBest=myNextBest,
                 stopping=myStopping,
                 increments=myIncrements,
                 cohortSize=mySize,
                 data=emptydata,
                 startingDose=3)

## define the true function
myTruth <- function(dose)
{
  model@prob(dose, alpha0=7, alpha1=8)
}

# Run the simulation on the desired design
# We only generate 1 trial outcomes here for illustration, for the actual study 
# this should be increased of course
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=1000)
time <- system.time(mySims <- simulate(design,
                                       args=NULL,
                                       truth=myTruth,
                                       nsim=1,
                                       seed=819,
                                       mcmcOptions=options,
                                       parallel=FALSE))[3]

# Summarize the Results of the Simulations
summary(mySims,truth=myTruth)

crmPack documentation built on Sept. 3, 2022, 1:05 a.m.