The empirical Bayes power prior of Gravestock and Held (2017)
for a binary endpoint: the power parameter maximizes the marginal
likelihood of the target data under the binomial power prior of
BinomialCPP, and the target data are analysed with that power prior.
Unlike GaussianGravestockEBPP, which uses the closed form of a normal
likelihood, the marginal likelihood is that of the binomial likelihoods of
both arms, computed on the lattice of binomial_power_prior_posterior().
Super classes
Model -> MCMCModel -> BinomialCPP -> BinomialGravestockEBPP
Public fields
empirical_bayesThe prior depends on the target data.
empirical_bayes_from_sampleThe prior is a function of the replicate's sample alone.
fixed_power_parameterThe power parameter changes between replicates.
methodMethod name.
Methods
Inherited methods
Model$calibrate_for_design()Model$check_data()Model$create()Model$estimate_bayesian_operating_characteristics()Model$estimate_frequentist_operating_characteristics()Model$hypothesis_space_transformation()Model$inference_cache_scope()Model$plot_pdfs()Model$plot_posterior_pdf()Model$plot_prior_pdf()Model$posterior_beta_mixture()Model$posterior_mean()Model$posterior_moments()Model$posterior_quantile()Model$posterior_to_RBesT()Model$print_model_summary()Model$prior_to_RBesT()Model$simulation_for_given_treatment_effect()Model$test_decision()Model$vectorised_replicate_inference()MCMCModel$check_mcmc_config()MCMCModel$compute_posterior_parameters()MCMCModel$credible_interval()MCMCModel$inference()MCMCModel$posterior_cdf()MCMCModel$posterior_ess()MCMCModel$posterior_median()MCMCModel$posterior_pdf()MCMCModel$prior_cdf()MCMCModel$prior_pdf()MCMCModel$sample_posterior()MCMCModel$sample_prior()MCMCModel$stan_sampler()MCMCModel$uses_quadrature()BinomialCPP$draw_mcmc_prior()BinomialCPP$prepare_data()BinomialCPP$prior_given_control_rate()BinomialCPP$quadrature_prior()BinomialCPP$summary_rows()
BinomialGravestockEBPP$new()
Initialize a BinomialGravestockEBPP model.
Usage
BinomialGravestockEBPP$new(prior, mcmc_config)BinomialGravestockEBPP$empirical_bayes_update()
Set the power parameter from the replicate's counts.
BinomialGravestockEBPP$quadrature_posterior()
The posterior on a grid, at the estimated power parameter.
Returns
A grid_posterior() list.
BinomialGravestockEBPP$prior_elir_ess()
ELIR effective sample size of the current prior,
interpolated over the power parameter as for the p-value-based power
prior; see binomial_power_prior_unit_elir().