BinomialNPP class

The normalized power prior for a binary endpoint, with the binomial likelihoods of the two arms of each study and a risk difference shared by the source and target studies, as in BinomialCPP. The power parameter has a Beta prior, specified by its mean and standard deviation as in GaussianNPP, and is integrated out exactly rather than through a normal approximation of the likelihood. The posterior is computed on a lattice of response rates; see binomial_npp_prior_kernels().

Super classes

Model -> MCMCModel -> BinomialLatticePrior -> BinomialNPP

Public fields

power_parameter_mean

Mean of the Beta prior on the power parameter.

power_parameter_std

Standard deviation of the Beta prior.

p

Shape parameter of the Beta prior.

q

Shape parameter of the Beta prior.

method

Name of the method.

Methods

Public methods

• BinomialNPP$summary_rows()

• BinomialNPP$new()

• BinomialNPP$kernels()

• BinomialNPP$kernel_key()

• BinomialNPP$compute_posterior_parameters()

• BinomialNPP$clone()

Inherited methods

• Model$calibrate_for_design()
• Model$check_data()
• Model$create()
• Model$empirical_bayes_update()
• 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$credible_interval()
• MCMCModel$draw_mcmc_prior()
• MCMCModel$inference()
• MCMCModel$posterior_cdf()
• MCMCModel$posterior_ess()
• MCMCModel$posterior_median()
• MCMCModel$posterior_pdf()
• MCMCModel$prepare_data()
• MCMCModel$prior_cdf()
• MCMCModel$prior_pdf()
• MCMCModel$sample_posterior()
• MCMCModel$sample_prior()
• MCMCModel$stan_sampler()
• MCMCModel$uses_quadrature()
• BinomialLatticePrior$prior_elir_ess()
• BinomialLatticePrior$prior_given_control_rate()
• BinomialLatticePrior$quadrature_posterior()
• BinomialLatticePrior$quadrature_prior()
• BinomialLatticePrior$source_counts()

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BinomialNPP$summary_rows()

Rows of the model summary, with the prior on the power parameter

Usage

BinomialNPP$summary_rows()

Returns

A data frame with columns Attribute and Value.

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BinomialNPP$new()

Initialize a BinomialNPP model.

Usage

BinomialNPP$new(prior, mcmc_config)

Arguments

prior

The prior object, with power_parameter_mean and power_parameter_std among its method parameters.

mcmc_config

The MCMC configuration; only the quadrature engine is supported.

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BinomialNPP$kernels()

The prior kernels, computed once per worker and shared.

Usage

BinomialNPP$kernels()

Returns

The output of binomial_npp_prior_kernels().

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BinomialNPP$kernel_key()

What identifies the prior, for the ELIR cache.

Usage

BinomialNPP$kernel_key()

Returns

A list.

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BinomialNPP$compute_posterior_parameters()

Record the posterior mean and standard deviation of the power parameter.

Usage

BinomialNPP$compute_posterior_parameters()

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BinomialNPP$clone()

The objects of this class are cloneable with this method.

Usage

BinomialNPP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.