getCritProb {BayesianMCPMod}

getCritProb

Description

This function calculates multiplicity adjusted critical values. The critical values are calculated in such a way that when using non-informative priors the actual error level for falsely declaring a significant trial in the Bayesian MCPMod is controlled (by the specified alpha level). Hereby optimal contrasts of the frequentist MCPMod are applied and two options can be distinguished

1. Frequentist approach: If only dose_weights are provided optimal contrast vectors are calculated from the regular MCPMod for these specific weights and the corresponding critical value for this set of contrasts is calculated via the critVal() function of the DoseFinding package.

2. Frequentist approach + re-estimation: If only a se_new_trial (i.e. the estimated variability per dose group of a new trial) is provided, optimal contrast vectors are calculated from the regular MCPMod for this specific vector of standard errors. Here as well the critical value for this set of contrasts is calculated via the critVal() function of the DoseFinding package.

Usage

getCritProb(
  mods,
  dose_levels,
  dose_weights = NULL,
  se_new_trial = NULL,
  alpha_crit_val = 0.025
)

Arguments

mods

An object of class "Mods" as specified in the DoseFinding package.

dose_levels

Vector containing the different dosage levels.

dose_weights

Vector specifying weights for the different doses, only required for Option i). Default NULL

se_new_trial

A vector of positive values, only required for Option ii). Default NULL

alpha_crit_val

Significance level. Default set to 0.025.

Value

Multiplicity adjusted critical value on the probability scale.

Examples

mods <- DoseFinding::Mods(linear      = NULL,
                          linlog      = 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)
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)