# Simulate dose escalation procedure using DLE and efficacy responses with samples This is a method to simulate dose escalation procedure using both DLE and efficacy responses. This is a method based on the `DualResponsesSamplesDesign` where DLE model 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(), 5L), ... ) ``` ## Arguments - `object`: the `DualResponsesSamplesDesign` 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`) (not with `EffFlexi`) the precision, the inverse of the variance of the efficacy responses - `trueSigma2`: (`number`) (only with `EffFlexi`) the true variance of the efficacy responses which must be a single positive scalar. - `trueSigma2betaW`: (`number`) (only with `EffFlexi`) the true variance for the random walk model used for smoothing. This must be a single positive scalar. - `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. - `mcmcOptions`: (McmcOptions ) object of class `McmcOptions`, giving the MCMC options for each evaluation in the trial. By default, the standard options are used - `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` or `PseudoDualFlexiSimulations` ## Examples ```r # nolint start ## 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), eff_dose = c(25, 300), nu = c(a = 1, b = 0.025), data = data ) ## The escalation rule using the 'NextBestMaxGainSamples' class mynextbest <- NextBestMaxGainSamples( prob_target_drt = 0.35, prob_target_eot = 0.3, derive = function(samples) { as.numeric(quantile(samples, prob = 0.3)) }, mg_derive = function(mg_samples) { as.numeric(quantile(mg_samples, 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) | StoppingMissingDose() ## Now specified the design with all the above information and starting with ## a dose of 25 ## Specified the design design <- DualResponsesSamplesDesign( nextBest = mynextbest, cohort_size = mySize, startingDose = 25, model = DLEmodel, eff_model = Effmodel, data = data, stopping = myStopping, increments = myIncrements ) ## specified the true DLE and efficacy curve 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))))) } ## simulate the trial for 10 times involving samples ## for illustration purposes, in reality larger number of burnin and samples shoud be used 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), eff_dose = c(25, 300), sigma2W = c(a = 0.1, b = 0.1), sigma2betaW = c(a = 20, b = 50), rw1 = FALSE, data = data ) ## Specified the design design <- DualResponsesSamplesDesign( nextBest = mynextbest, cohort_size = mySize, startingDose = 25, model = DLEmodel, eff_model = Effmodel, data = data, stopping = myStopping, increments = myIncrements ) ## specified the true DLE curve and the true expected efficacy values at all dose levels myTruthDLE <- probFunction(DLEmodel, 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 ) # nolint end ```

simulate-DualResponsesSamplesDesign-method function