# Simulate dose escalation procedure using both DLE and efficacy responses without samples This is a method to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesDesign` where DLE model 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(), 5L), ... ) ``` ## Arguments - `object`: the `DualResponsesDesign` object we want to simulate the data from - `nsim`: (`count`) the number of simulations (default: 1) - `seed`: see `set_seed()` - `trueDLE`: (`function`) 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`: (`function`) a function which takes as input a dose (vector) and returns the expected efficacy responses (vector). Additional arguments can be supplied in `args`. - `trueNu`: (`number`) the precision, the inverse of the variance of the efficacy responses - `args`: (`data.frame`) 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`: (`flag`) 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`: (`flag`) should the simulation runs be parallelized across the clusters of the computer? (not default) - `nCores`: (`count`) 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 # nolint start ## 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), eff_dose = c(25, 300), nu = c(a = 1, b = 0.025), data = data ) ## The escalation rule using the 'NextBestMaxGain' class mynextbest <- NextBestMaxGain( prob_target_drt = 0.35, prob_target_eot = 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) | StoppingMissingDose() ## 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, eff_model = Effmodel, stopping = myStopping, increments = myIncrements, cohort_size = mySize, data = data, startingDose = 25 ) ## Specify the true DLE and efficacy curves myTruthDLE <- probFunction(DLEmodel, phi1 = -53.66584, phi2 = 10.50499) myTruthEff <- efficacyFunction(Effmodel, 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 ) # nolint end ```

simulate-DualResponsesDesign-method function