# This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesSamplesDesign` where DLEmodel used are of `ModelTox` class object and efficacy model used are of `ModelEff` class object (special case is `EffFlexi` class model object). In addition, DLE and efficacy samples are involved or generated in the simulation process ```r ## S4 method for signature 'DualResponsesSamplesDesign' simulate( object, nsim = 1L, seed = NULL, trueDLE, trueEff, trueNu = NULL, trueSigma2 = NULL, trueSigma2betaW = NULL, args = NULL, firstSeparate = FALSE, mcmcOptions = McmcOptions(), parallel = FALSE, nCores = min(parallel::detectCores(), 5), ... ) ``` ## Arguments - `object`: the `DualResponsesSamplesDesign` object we want to simulate the data from - `nsim`: the number of simulations (default :1) - `seed`: see `setSeed` - `trueDLE`: a function which takes as input a dose (vector) and returns the true probability (vector) of the occurrence of a DLE. Additional arguments can be supplied in `args`. - `trueEff`: a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in `args`. - `trueNu`: (not with `EffFlexi`) the precision, the inverse of the variance of the efficacy responses - `trueSigma2`: (only with `EffFlexi`) the true variance of the efficacy responses which must be a single positive scalar. - `trueSigma2betaW`: (only with `EffFlexi`) the true variance for the random walk model used for smoothing. This must be a single postive scalar. - `args`: data frame with arguments for the `trueDLE` and `trueEff` function. The column names correspond to the argument names, the rows to the values of the arguments. The rows are appropriately recycled in the `nsim` simulations. - `firstSeparate`: enroll the first patient separately from the rest of the cohort? (not default) If yes, the cohort will be closed if a DLT occurs in this patient. - `mcmcOptions`: object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used - `parallel`: should the simulation runs be parallelized across the clusters of the computer? (not default) - `nCores`: how many cores should be used for parallel computing? Defaults to the number of cores on the machine, maximum 5. - ``...``: not used ## Returns an object of class `PseudoDualSimulations` or `PseudoDualFlexiSimulations` ## Examples ```r ##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE ## and efficacy samples are used 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 '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 10 subjects are treated (only for illustration such a low ##sample size) myStopping <- StoppingMinPatients(nPatients=10) ##Now specified the design with all the above information and starting with ##a dose of 25 ##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 and efficacy curve myTruthDLE<- function(dose) { DLEmodel@prob(dose, phi1=-53.66584, phi2=10.50499) } myTruthEff<- function(dose) {Effmodel@ExpEff(dose,theta1=-4.818429,theta2=3.653058) } ##The true gain curve can also be seen myTruthGain <- function(dose) {return((myTruthEff(dose))/(1+(myTruthDLE(dose)/(1-myTruthDLE(dose)))))} ##simulate the trial for 10 times involving samples ##for illustration purpose we use 10 burn-ins to generate 50 samples options<-McmcOptions(burnin=10,step=1,samples=50) ##For illustration purpose only 1 simulations are produced (nsim=1). mySim<-simulate(design, args=NULL, trueDLE=myTruthDLE, trueEff=myTruthEff, trueNu=1/0.025, nsim=1, mcmcOptions=options, seed=819, parallel=FALSE) ##Simulate dose-escalation procedure based on DLE and efficacy responses where DLE ## and efficacy samples are used ## when the efficacy model is of 'EffFlexi' class 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 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) ##The true gain curve can also be seen d1 <- data@doseGrid myTruthGain <- (myTruthEff)/(1+(myTruthDLE(d1)/(1-myTruthDLE(d1)))) mySim<-simulate(object=design, args=NULL, trueDLE=myTruthDLE, trueEff=myTruthEff, trueSigma2=0.025, trueSigma2betaW=1, mcmcOptions=options, nsim=1, seed=819, parallel=FALSE) ```

simulate-DualResponsesSamplesDesign-method function