summary-PseudoDualSimulations-method: Summary for Pseudo Dual responses simulations, relative to a...

In crmPack: Object-Oriented Implementation of CRM Designs

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Description

Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model)

Usage

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

Arguments

object	the PseudoDualSimulations object we want to summarize
trueDLE	a function which takes as input a dose (vector) and returns the true probability (vector) of DLE
trueEff	a function which takes as input a dose (vector) and returns the mean efficacy value(s) (vector).
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

##obtain the plot for the simulation results
##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
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)

##The efficacy model of 'ModelEff' (e.g 'Effloglog') class 
Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),
                    nu=c(a=1,b=0.025),data=data)

##The escalation rule using the 'NextBestMaxGain' class
mynextbest<-NextBestMaxGain(DLEDuringTrialtarget=0.35,
                            DLEEndOfTrialtarget=0.3)


##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 36 subjects are treated
myStopping <- StoppingMinPatients(nPatients=36)
##Now specified the design with all the above information and starting with a dose of 25

##Specified the design(for details please refer to the 'DualResponsesDesign' example)
design <- DualResponsesDesign(nextBest=mynextbest,
                              model=DLEmodel,
                              Effmodel=Effmodel,
                              stopping=myStopping,
                              increments=myIncrements,
                              cohortSize=mySize,
                              data=data,startingDose=25)
##Specify the true DLE and efficacy curves
myTruthDLE<- function(dose)
{ DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499)
}

myTruthEff<- function(dose)
{Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058)
}

## Then specified the simulations and generate the trial for 2 times
mySim <-simulate(object=design,
                 args=NULL,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff,
                 trueNu=1/0.025,
                 nsim=2,
                 seed=819,
                 parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)
##If DLE and efficacy samples are involved
##Please refer to design-method 'simulate DualResponsesSamplesDesign' examples for details
##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)})
##specified the design
design <- DualResponsesSamplesDesign(nextBest=mynextbest,
                                     cohortSize=mySize,
                                     startingDose=25,
                                     model=DLEmodel,
                                     Effmodel=Effmodel,
                                     data=data,
                                     stopping=myStopping,
                                     increments=myIncrements)
##options for MCMC
##For illustration purpose, we will use 50 burn-ins to generate 200 samples
options<-McmcOptions(burnin=50,step=2,samples=200)
##The simulations
##For illustration purpose only 2 simulation is produced (nsim=2). 
mySim<-simulate(design,
                args=NULL,
                trueDLE=myTruthDLE,
                trueEff=myTruthEff,
                trueNu=1/0.025,
                nsim=2,
                mcmcOptions=options,
                seed=819,
                parallel=FALSE)

##Then produce a summary of your simulations
summary(mySim,
        trueDLE=myTruthDLE,
        trueEff=myTruthEff)

crmPack documentation built on June 26, 2024, 5:07 p.m.