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
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.
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.
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)