# Summary for Pseudo Dual responses simulations, relative to a given pseudo DLE and efficacy model (except the EffFlexi class model) ```r ## 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` ## Returns an object of class `PseudoDualSimulationsSummary` ## Examples ```r ##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) ```

summary-PseudoDualSimulations-method function