# This is a methods to simulate dose escalation procedure only using the DLE responses. This is a method based on the `TDDesign` where model used are of `ModelTox` class object and no samples are involved. ```r ## S4 method for signature 'TDDesign' simulate( object, nsim = 1L, seed = NULL, truth, args = NULL, firstSeparate = FALSE, parallel = FALSE, nCores = min(parallel::detectCores(), 5), ... ) ``` ## Arguments - `object`: the `TDDesign` object we want to simulate the data from - `nsim`: the number of simulations (default :1) - `seed`: see `setSeed` - `truth`: 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`. - `args`: data frame with arguments for the `truth` 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. In order to produce outcomes from the posterior predictive distribution, e.g, pass an `object` that contains the data observed so far, `truth` contains the `prob` function from the model in `object`, and `args` contains posterior samples from the model. - `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 `PseudoSimulations` @export @keywords methods ## Examples ```r ##Simulate dose-escalation procedure based only on DLE responses and no DLE samples are used ##The design comprises a model, 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 <- Data(doseGrid=seq(25,300,25)) ##The design only incorporate DLE responses and DLE samples are involved ##Specified the model of 'ModelTox' class eg 'LogisticIndepBeta' class model model<-LogisticIndepBeta(binDLE=c(1.05,1.8),DLEweights=c(3,3),DLEdose=c(25,300),data=data) ##Then the escalation rule tdNextBest <- NextBestTD(targetDuringTrial=0.35, targetEndOfTrial=0.3) doseRecommendation<-nextBest(tdNextBest, doselimit=max(data@doseGrid), model=model, data=data) ##Then the starting data, an empty data set emptydata<-Data(doseGrid=seq(25,300,25)) ## The cohort size, size of 3 subjects mySize <-CohortSizeConst(size=3) ##Deifne the increments for the dose-escalation process ##The maximum increase of 200% for doses up to the maximum of the dose specified in the doseGrid ##The maximum increase of 200% for dose above the maximum of the dose specified in the doseGrid ##This is to specified a maximum of 3-fold restriction in dose-esclation myIncrements<-IncrementsRelative(intervals=c(min(data@doseGrid),max(data@doseGrid)), increments=c(2,2)) ##Specified the stopping rule e.g stop when the maximum sample size of 36 patients has been reached myStopping <- StoppingMinPatients(nPatients=36) ##Specified the design(for details please refer to the 'TDDesign' example) design <- TDDesign(model=model, nextBest=tdNextBest, stopping=myStopping, increments=myIncrements, cohortSize=mySize, data=data,startingDose=25) ##Specify the truth of the DLE responses myTruth <- function(dose) { model@prob(dose, phi1=-53.66584, phi2=10.50499) } ##then plot the truth to see how the truth dose-DLE curve look like curve(myTruth(x), from=0, to=300,ylim=c(0,1)) ##For illustration purpose only 1 simulation is produced (nsim=1). mySim <- simulate(object=design, args=NULL, truth=myTruth, nsim=1, seed=819, parallel=FALSE) ```

simulate-TDDesign-method function