BayesianMCPMod (version 1.2.0)

getContr: getContr

Description

This function calculates contrast vectors that are optimal for detecting certain alternatives via applying the function optContr() of the DoseFinding package. Hereby, 4 different options can be distinguished that are automatically executed based on the input that is provided

1. Bayesian approach: If dose_weights and a prior_list are provided an optimized contrasts for the posterior sample size is calculated. In detail, in a first step the dose_weights (typically the number of patients per dose group) and the prior information is combined by calculating for each dose group a posterior effective sample. Based on this posterior effective sample sizes the allocation ratio is derived, which allows for a calculation on pseudo-optimal contrasts via regular MCPMod are calculated from the regular MCPMod for these specific weights

2. Frequentist approach: If only dose_weights are provided optimal contrast vectors are calculated from the regular MCPMod for these specific weights

3. Bayesian approach + re-estimation: If only a cov_posterior (i.e. variability of the posterior distribution) is provided, pseudo-optimal contrasts based on these posterior weights will be calculated

4. Frequentist approach+re-estimation: If only a cov_new_trial (i.e. the estimated variability of a new trial) is provided, optimal contrast vectors are calculated from the regular MCPMod for this specific covariance matrix.

Usage

getContr(
  mods,
  dose_levels,
  dose_weights = NULL,
  prior_list = NULL,
  cov_posterior = NULL,
  cov_new_trial = NULL
)

Value

An object of class 'optContr' as provided by the function 'DoseFinding::optContr()'.

Arguments

mods

An object of class 'Mods' as created by the function 'DoseFinding::Mods()'

dose_levels

Vector containing the different dosage levels.

dose_weights

Vector specifying weights for the different doses. Please note that in case this information is provided together with a prior (i.e. Option 1) is planned these two inputs should be provided on the same scale (e.g. patient numbers). Default NULL

prior_list

A list of objects of class 'normMix' as created with 'RBesT::mixnorm()'. Only required as input for Option 1. Default NULL

cov_posterior

A covariance matrix with information about the variability of the posterior distribution, only required for Option 3. Default NULL

cov_new_trial

A covariance matrix with information about the observed variability, only required for Option 4. Default NULL

Examples

dose_levels  <- c(0, 0.5, 2, 4, 8)
mods <- DoseFinding::Mods(
  linear      = NULL,
  emax        = c(0.5, 1.2),
  exponential = 2,
  doses       = dose_levels,
  maxEff      = 6)
cov_posterior <- diag(c(2.8, 3, 2.5, 3.5, 4)^2)

contr_mat <- getContr(
  mods         = mods,
  dose_levels  = dose_levels,
  cov_posterior = cov_posterior)