BayesianMCPMod (version 1.2.0)

assessDesign: 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,
  modeling = FALSE,
  simple = TRUE,
  avg_fit = TRUE,
  reestimate = FALSE,
  contr = NULL,
  dr_means = NULL,
  delta = NULL,
  evidence_level = NULL,
  med_selection = c("avgFit", "bestFit")
)

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

Arguments

n_patients

Vector specifying the planned number of patients per dose group. A minimum of 2 patients are required in each 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.

modeling

Boolean variable defining whether the Mod part of Bayesian MCP-Mod will be performed in the assessment. More heavy on resources. Default FALSE.

simple

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

avg_fit

Boolean variable, defining whether an average fit (based on generalized AIC weights) should be performed in addition to the individual models. Default TRUE.

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

delta

A numeric value for the threshold Delta for the MED assessment. If NULL, no MED assessment is performed. Default NULL.

evidence_level

A numeric value between 0 and 1 for the evidence level gamma for the MED assessment. Only required for Bayesian MED assessment, see ?getMED for details. Default NULL.

med_selection

A string, either "avgFit" or "bestFit", for the method of MED selection. Default "avgFit".

Examples

mods <- DoseFinding::Mods(linear      = 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 example run time

success_probabilities

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

# with MED estimation without bootstrapping
# see ?getMED for details

success_probabilities <- assessDesign(
  n_patients     = n_patients,
  mods           = mods,
  prior_list     = prior_list,
  sd             = sd,
  modeling       = TRUE,
  n_sim          = 10, # speed up example run time
  delta          = 7)

  success_probabilities

# with MED estimation with bootstrapping

success_probabilities <- assessDesign(
  n_patients     = n_patients,
  mods           = mods,
  prior_list     = prior_list,
  sd             = sd,
  modeling       = TRUE,
  n_sim          = 10, # speed up example run time
  delta          = 7,
  evidence_level = 0.8)

  success_probabilities

}