# Compute Gain Values based on Pseudo DLE and a Pseudo Efficacy Models and Using Optional Samples. ## Description ```r gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...) ## S4 method for signature 'numeric,ModelTox,Samples,ModelEff,Samples' gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...) ## S4 method for signature 'numeric,ModelTox,missing,Effloglog,missing' gain(dose, model_dle, samples_dle, model_eff, samples_eff, ...) ``` ## Arguments - `dose`: (`number` or `numeric`) the dose which is targeted. The following recycling rule applies when samples are not missing: vectors of size 1 will be recycled to the size of the sample. Otherwise, `dose` must have the same size as the sample. - `model_dle`: (`ModelTox`) pseudo DLE (dose-limiting events)/toxicity model. - `samples_dle`: (`Samples`) the samples of model's parameters that will be used to compute toxicity probabilities. Can also be missing for some models. - `model_eff`: (`ModelEff`) the efficacy model with pseudo data prior. - `samples_eff`: (`Samples`) samples of model's parameters that will be used to compute expected efficacy values. Can also be missing for some models. - `...`: not used. ## Returns The gain values. ## Details This function computes the gain values for a given dose level, pseudo DLE and Efficacy models as well as a given DLE and Efficacy samples. ## Functions * `gain( dose = numeric, model_dle = ModelTox, samples_dle = Samples,model_eff = ModelEff, samples_eff = Samples )`: * `gain( dose = numeric, model_dle = ModelTox, samples_dle = missing,model_eff = Effloglog, samples_eff = missing )`: Compute the gain value for a given dose level, pseudo DLE and Efficacy models without DLE and the Efficacy samples. ## Examples ```r # Obtain the gain value for a given dose, a pseudo DLE and efficacy models # as well as DLE and efficacy samples. emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE) mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200) # DLE model and samples. model_dle <- LogisticIndepBeta( binDLE = c(1.05, 1.8), DLEweights = c(3, 3), DLEdose = c(25, 300), data = emptydata ) samples_dle <- mcmc(emptydata, model_dle, mcmc_opts) # Efficacy model (Effloglog) and samples. model_effloglog <- Effloglog( eff = c(1.223, 2.513), eff_dose = c(25, 300), nu = c(a = 1, b = 0.025), data = emptydata ) samples_effloglog <- mcmc(emptydata, model_effloglog, mcmc_opts) # Gain values for dose level 75 and Effloglog efficacy model. gain( dose = 75, model_dle = model_dle, samples_dle = samples_dle, model_eff = model_effloglog, samples_eff = samples_effloglog ) # Efficacy model (EffFlexi) and samples. model_effflexi <- 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 = emptydata ) samples_effflexi <- mcmc(emptydata, model_effflexi, mcmc_opts) # Gain values for dose level 75 and EffFlexi efficacy model. gain( dose = 75, model_dle = model_dle, samples_dle = samples_dle, model_eff = model_effflexi, samples_eff = samples_effflexi ) # Obtain the gain value for a given dose, a pseudo DLE and efficacy models # without DLE and efficacy samples. emptydata <- DataDual(doseGrid = seq(25, 300, 25), placebo = FALSE) data <- Data(doseGrid = seq(25, 300, 25), placebo = FALSE) mcmc_opts <- McmcOptions(burnin = 100, step = 2, samples = 200) # DLE model and samples. model_dle <- LogisticIndepBeta( binDLE = c(1.05, 1.8), DLEweights = c(3, 3), DLEdose = c(25, 300), data = data ) # Efficacy model and samples. model_eff <- Effloglog( eff = c(1.223, 2.513), eff_dose = c(25, 300), nu = c(a = 1, b = 0.025), data = emptydata ) # Gain value for dose level 75. gain( dose = 75, model_dle = model_dle, model_eff = model_eff ) ```

gain function