getBootstrapSamples {BayesianMCPMod}

getBootstrapSamples

Description

A function for the calculation of bootstrapped model predictions. Samples from the posterior distribution are drawn (via the RBesT function rmix()) and for every sample the simplified fitting step (see getModelFits() function) and a prediction is performed. These fits are then used to identify the specified quantiles. This approach can be considered as the Bayesian equivalent of the frequentist bootstrap approach described in O'Quigley et al. (2017). Instead of drawing n bootstrap samples from the sampling distribution of the trial dose-response estimates, here the samples are directly taken from the posterior distribution.

Usage

getBootstrapSamples(model_fits, n_samples = 1000, doses = NULL, avg_fit = TRUE)

Arguments

model_fits

An object of class modelFits, i.e. information about fitted models & corresponding model coefficients as well as the posterior distribution that was the basis for the model fitting

n_samples

Number of samples that should be drawn as basis for the bootstrapped quantiles

doses

A vector of doses for which a prediction should be performed

avg_fit

Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE.

Value

A data frame with columns for model, dose, and bootstrapped samples

References

O'Quigley J, Iasonos A, Bornkamp B. 2017. Handbook of Methods for Designing, Monitoring, and Analyzing Dose-Finding Trials (1st ed.). Chapman and Hall/CRC. doi:10.1201/9781315151984

Examples

posterior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 1), sigma = 2),
                       DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 3, s = 1.2), sigma = 2),
                       DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 4, s = 1.5), sigma = 2) ,  
                       DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 6, s = 1.2), sigma = 2) ,
                       DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 6.5, s = 1.1), sigma = 2))
models         <- c("exponential", "linear")
dose_levels    <- c(0, 1, 2, 4, 8)
fit            <- getModelFits(models      = models,
                               posterior   = posterior_list,
                               dose_levels = dose_levels,
                               simple      = TRUE)
                               
getBootstrapSamples(model_fits = fit,
                    n_samples  = 10, # speeding up example run time
                    doses      = c(0, 6, 8))