# This is a methods to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesDesign` where DLEmodel used are of `ModelTox` class object and efficacy model used are of `ModelEff` class object. In addition, no DLE and efficacy samples are involved or generated in the simulation process ```r ## S4 method for signature 'DualResponsesDesign' simulate( object, nsim = 1L, seed = NULL, trueDLE, trueEff, trueNu, args = NULL, firstSeparate = FALSE, parallel = FALSE, nCores = min(parallel::detectCores(), 5), ... ) ``` ## Arguments - `object`: the `DualResponsesDesign` 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`: the precision, the inverse of the variance of the efficacy responses - `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. - `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` ## Examples ```r ##Simulate dose-escalation procedure based on DLE and efficacy responses where no DLE ## and efficacy 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<- 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)))))} ## Then specified the simulations and generate the trial ##For illustration purpose only 1 simulation is produced (nsim=1). options<-McmcOptions(burnin=100,step=2,samples=200) mySim <-simulate(object=design, args=NULL, trueDLE=myTruthDLE, trueEff=myTruthEff, trueNu=1/0.025, nsim=1, seed=819, parallel=FALSE) ```

simulate-DualResponsesDesign-method function