class # This is a class of design based on DLE responses using the `LogisticIndepBeta` model model and efficacy responses using `ModelEff` model class with DLE and efficacy samples.It contain all slots in `RuleDesign` and `TDsamplesDesign` class object ## Slots - **`data`**: the data set of `DataDual` class object - **`Effmodel`**: the pseudo efficacy model to be used, an object class of `ModelEff` ## Examples ```r ##Construct the DualResponsesSamplesDesign for simulations ##The design comprises the DLE and efficacy models, the escalation rule, starting data, ##a cohort size and a starting dose ##Define your data set first using an empty data set ## with dose levels from 25 to 300 with increments 25 data <- DataDual(doseGrid=seq(25,300,25),placebo=FALSE) ## First for the DLE model and DLE samples ## The DLE model must be of 'ModelTox' ## (e.g 'LogisticIndepBeta') class and ## DLEsamples of 'Samples' class options<-McmcOptions(burnin=100,step=2,samples=200) DLEmodel <- LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3), DLEdose=c(25,300),data=data) DLEsamples<-mcmc(data,DLEmodel,options) ##The efficacy model of 'ModelEff' (e.g 'Effloglog') class and the efficacy samples Effmodel<-Effloglog(Eff=c(1.223,2.513),Effdose=c(25,300),nu=c(a=1,b=0.025),data=data,c=0) Effsamples<-mcmc(data,Effmodel,options) ##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 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 design <- DualResponsesSamplesDesign(nextBest=mynextbest, cohortSize=mySize, startingDose=25, model=DLEmodel, Effmodel=Effmodel, data=data, stopping=myStopping, increments=myIncrements) ```

DualResponsesSamplesDesign-class function