BayesianMCPMod (version 1.2.0)

performBayesianMCP: performBayesianMCP

Description

Performs Bayesian MCP Test step, as described in Fleischer et al. (2022). Tests for a dose-response effect using a model-based multiple contrast test based on the (provided) posterior distribution. In particular for every dose-response candidate the posterior probability is calculated that the contrast is bigger than 0 (based on the posterior distribution of the dose groups). In order to obtain significant test decision we consider the maximum of the posterior probabilities across the different models. This maximum is compared with a (multiplicity adjusted) critical value (on the probability scale).

Usage

performBayesianMCP(posterior_list, contr, crit_prob_adj)

Value

Bayesian MCP test result, with information about p-values for the individual dose-response shapes and overall significance

Arguments

posterior_list

An object derived with getPosterior with information about the (mixture) posterior distribution per dose group

contr

An object of class 'optContr' as created by the getContr() function. It contains the contrast matrix to be used for the testing step.

crit_prob_adj

A getCritProb object, specifying the critical value to be used for the testing (on the probability scale)

References

Fleischer F, Bossert S, Deng Q, Loley C, Gierse J. 2022. Bayesian MCPMod. Pharmaceutical Statistics. 21(3): 654-670. doi:10.1002/pst.2193

Examples

mods <- DoseFinding::Mods(linear      = NULL,
                          emax        = c(0.5, 1.2),
                          exponential = 2,
                          doses       = c(0, 0.5, 2,4, 8))
dose_levels  <- c(0, 0.5, 2, 4, 8)
sd_posterior <- c(2.8,3,2.5,3.5,4)
contr_mat <- getContr(
  mods          = mods,
  dose_levels   = dose_levels,
  cov_posterior = diag(sd_posterior)^2)
critVal <- getCritProb(
  mods           = mods,
  dose_weights   = c(50, 50, 50, 50, 50), #reflecting the planned sample size
  dose_levels    = dose_levels,
  alpha_crit_val = 0.05)
prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 5), sigma = 2),
                   DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
                   DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2) ,
                   DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2) ,
                   DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2))
mu <- c(0, 1, 1.5, 2, 2.5)
S_hat <- diag(c(5, 4, 6, 7, 8)^2)
posterior_list <- getPosterior(
  prior_list = prior_list,
  mu_hat     = mu,
  S_hat      = S_hat,
  calc_ess   = TRUE)

performBayesianMCP(posterior_list = posterior_list,
                   contr          = contr_mat,
                   crit_prob_adj  = critVal)