show-PseudoDualSimulationsSummary-method: Show the summary of Pseudo Dual simulations summary

In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

Description

Show the summary of Pseudo Dual simulations summary

Usage

## S4 method for signature 'PseudoDualSimulationsSummary'
show(object)

Arguments

object

the PseudoDualSimulationsSummary object we want to print

Value

invisibly returns a data frame of the results with one row and appropriate column names

Examples

# nolint start

##If DLE and efficacy responses are considered in the simulations
##Specified your simulations when no samples are used
## we need a data object with doses >= 1:
data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE)
##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,c=0)

##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 <- probFunction(DLEmodel, 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 illustration purpose only 2 simulation is produced (nsim=2).
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
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                 trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)




##If DLE and efficacy samples are involved
##The escalation rule using the 'NextBestMaxGainSamples' class
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 design of 'DualResponsesSamplesDesign' class
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 we only simulate 2 trials (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
MYSUM <- summary(mySim,
                 trueDLE=myTruthDLE,
                trueEff=myTruthEff)
##Then show the summary in data frame for your simulations
show(MYSUM)

# nolint end

0liver0815/onc-crmpack-test documentation built on Feb. 19, 2022, 12:25 a.m.