assessDesign {BayesianMCPMod}

assessDesign .

Description

This function performs simulation based trial design evaluations for a set of specified dose-response models

Usage

assessDesign(
  n_patients,
  mods,
  prior_list,
  sd,
  n_sim = 1000,
  alpha_crit_val = 0.05,
  simple = TRUE,
  reestimate = FALSE,
  contr = NULL,
  dr_means = NULL
)

Arguments

n_patients

Vector specifying the planned number of patients per dose group

mods

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

prior_list

A prior_list object specifying the utilized prior for the different dose groups

sd

A positive value, specification of assumed sd

n_sim

Number of simulations to be performed

alpha_crit_val

(Un-adjusted) Critical value to be used for the MCP testing step. Passed to the getCritProb() function for the calculation of adjusted critical values (on the probability scale). Default is 0.05.

simple

Boolean variable defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.

reestimate

Boolean variable defining whether critical value should be calculated with re-estimated contrasts (see getCritProb function for more details). Default FALSE

contr

An object of class 'optContr' as created by the getContr() function. Allows specification of a fixed contrasts matrix. Default NULL

dr_means

A vector, allows specification of individual (not model based) assumed effects per dose group. Default NULL

Value

Returns success probabilities for the different assumed dose-response shapes, attributes also includes information around average success rate (across all assumed models) and prior Effective sample size

Examples

if (interactive()) { # takes typically > 5 seconds

mods <- DoseFinding::Mods(linear      = NULL,
                          linlog      = NULL,
                          emax        = c(0.5, 1.2),
                          exponential = 2,
                          doses       = c(0, 0.5, 2,4, 8),
                          maxEff      = 6)
sd <- 12
prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 12), 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))
n_patients <- c(40, 60, 60, 60, 60)

success_probabilities <- assessDesign(
  n_patients  = n_patients,
  mods        = mods,
  prior_list  = prior_list,
  sd          = sd,
  n_sim       = 1e2) # speed up exammple run time

success_probabilities

}