bhmbasket (version 1.0.0)

getEstimates: getEstimates

Description

This function calculates the point estimates and credible intervals per cohort, as well as estimates of the biases and the mean squared errors of the point estimates per cohort.

Usage

getEstimates(
  analyses_list,
  add_parameters = NULL,
  point_estimator = "median",
  alpha_level = 0.05
)

Value

A named list of matrices of estimates of response rates and credible intervals. Estimates of bias and MSE are included for response rate estimates of simulated trials.

Arguments

analyses_list

An object of class analysis_list, as created with performAnalyses

add_parameters

A vector of strings naming additional parameters from the Bayesian hierarchical models, e.g. c('mu', 'tau'). If NULL, no additional parameters will be evaluated, Default: NULL

point_estimator

A string indicating the type of estimator used for calculation of bias and MSE. Must be one of 'median' or 'mean'

alpha_level

A numeric in (0, 1) for the level of the credible interval. Only values corresponding to quantiles saved in performAnalyses will work, Default: 0.05

Author

Stephan Wojciekowski

Details

Bias and MSE will only be calculated for response rate estimates of simulated trials. For additional parameters, bias and MSE will not be calculated.

Possible additional parameters are for the Bayesian hierarchical models are c('mu', 'tau') for 'berry', 'exnex', and 'exnex_adj'. The latter two models can also access the posterior weights paste0("w_", seq_len(n_cohorts)).

See Also

createTrial simulateScenarios performAnalyses

Examples

  scenarios_list <- simulateScenarios(
    n_subjects_list     = list(c(10, 20, 30)),
    response_rates_list = list(c(0.1, 0.2, 3)),
    n_trials            = 10)

  analyses_list <- performAnalyses(
    scenario_list       = scenarios_list,
    target_rates        = c(0.1, 0.1, 0.1),
    calc_differences    = matrix(c(3, 2, 2, 1), ncol = 2),
    n_mcmc_iterations   = 100)

  getEstimates(analyses_list)
  getEstimates(analyses_list   = analyses_list,
               add_parameters  = c("mu", "tau", "w_1", "w_2", "w_3"),
               point_estimator = "mean",
               alpha_level     = 0.1)

  outcome <- createTrial(
    n_subjects          = c(10, 20, 30),
    n_responders        = c( 1,  2,  3))

  outcome_analysis <- performAnalyses(
    scenario_list       = outcome,
    target_rates        = c(0.1, 0.1, 0.1),
    n_mcmc_iterations   = 100)

  getEstimates(outcome_analysis)
  getEstimates(analyses_list  = outcome_analysis,
               add_parameters = c("mu", "w_1", "w_2", "w_3"))