A function to return bootstrap samples from the fitted dose-response models. 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 samples are returned by this function. 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.
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
- doses
A vector of doses for which a prediction should be performed
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)
model_fits <- getModelFits(models = models,
posterior = posterior_list,
dose_levels = dose_levels,
simple = TRUE)
bs_samples <- getBootstrapSamples(model_fits = model_fits,
n_samples = 10, # speeding up example run time
doses = c(0, 6, 8))
bs_samples
#> # A tibble: 90 × 3
#> models doses sample
#> <chr> <dbl> <dbl>
#> 1 exponential 0 1.15
#> 2 linear 0 0.923
#> 3 avgFit 0 0.923
#> 4 exponential 6 5.85
#> 5 linear 6 6.11
#> 6 avgFit 6 6.11
#> 7 exponential 8 7.84
#> 8 linear 8 7.83
#> 9 avgFit 8 7.83
#> 10 exponential 0 2.93
#> # ℹ 80 more rows