## S4 method for signature 'SimulationsSummary'show(object)
Arguments
object: the SimulationsSummary object we want to print
Returns
invisibly returns a data frame of the results with one row and appropriate column names
Examples
# Define the dose-gridemptydata <- 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 stoppingmyStopping1 <- 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 incrementsmyIncrements <- IncrementsRelative(intervals=c(0,20), increments=c(1,0.33))# Initialize the designdesign <- Design(model=model, nextBest=myNextBest, stopping=myStopping, increments=myIncrements, cohortSize=mySize, data=emptydata, startingDose=3)## define the true functionmyTruth <-function(dose){ model@prob(dose, alpha0=7, alpha1=8)}# 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 courseoptions <- 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]# Show the Summary of the Simulationsshow(summary(mySims,truth=myTruth))