# Summary for Pseudo Dual responses simulations given a pseudo DLE model and the Flexible efficacy model. ```r ## 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` ## Returns an object of class `PseudoDualSimulationsSummary` ## Examples ```r ##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) ```

summary-PseudoDualFlexiSimulations-method function