summary,PseudoDualFlexiSimulations-method {crmPack}

Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

Description

Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model.

Usage

## S4 method for signature 'PseudoDualFlexiSimulations'
summary(
  object,
  trueDLE,
  trueEff,
  targetEndOfTrial = 0.3,
  targetDuringTrial = 0.35,
  ...
)

Arguments

object

the PseudoDualFlexiSimulations object we want to summarize

trueDLE

a function which takes as input a dose (vector) and returns the true probability of DLE (vector)

trueEff

a vector which takes as input the true mean efficacy values at all dose levels (in order)

targetEndOfTrial

the target probability of DLE that are used at the end of a trial. Default at 0.3.

targetDuringTrial

the target probability of DLE that are used during the trial. Default at 0.35.

...

Additional arguments can be supplied here for trueDLE and trueEff

Value

an object of class PseudoDualSimulationsSummary

Examples

##If DLE and efficacy responses are considered in the simulations and the 'EffFlexi' class is used 
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25))
##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)

## for the efficacy model
Effmodel<- EffFlexi(Eff=c(1.223, 2.513),Effdose=c(25,300),
                    sigma2=c(a=0.1,b=0.1),sigma2betaW=c(a=20,b=50),smooth="RW2",data=data)


##specified the next best
mynextbest<-NextBestMaxGainSamples(DLEDuringTrialtarget=0.35,
                                   DLEEndOfTrialtarget=0.3,
                                   TDderive=function(TDsamples){
                                     quantile(TDsamples,prob=0.3)},
                                   Gstarderive=function(Gstarsamples){
                                     quantile(Gstarsamples,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:
## very low sample size is just for illustration here
myStopping <- StoppingMinPatients(nPatients=10)

##Specified the design 
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##specified the true DLE curve and the true expected efficacy values at all dose levels
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, 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)


##specify the options for MCMC
#For illustration purpose, we use 10 burn-in and generate 100 samples
options<-McmcOptions(burnin=10,step=1,samples=100)
##The simulation
##For illustration purpose only 1 simulation is produced (nsim=1). 
mySim<-simulate(object=design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueSigma2=0.025,
                trueSigma2betaW=1,
                nsim=1,
                seed=819,
                parallel=FALSE,
                mcmcOptions=options)
##summarize the simulation results
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)