prior_pattern: An internal function to change the hyperprior parameters in...

In missingHE: Missing Outcome Data in Health Economic Evaluation

An internal function to change the hyperprior parameters in the pattern mixture model provided by the user depending on the type of missingness mechanism and outcome distributions assumed

Description

This function modifies default hyper prior parameter values in the type of pattern mixture model selected according to the type of missingness mechanism and distributions for the outcomes assumed.

Usage

prior_pattern(type, dist_e, dist_c, model_txt_info, model_string_jags)

Arguments

type	Type of missingness mechanism assumed. Choices are Missing At Random (MAR), Missing Not At Random for the effects (MNAR_eff), Missing Not At Random for the costs (MNAR_cost), and Missing Not At Random for both (MNAR). For a complete list of all available hyper parameters and types of models see the manual.
dist_e	distribution assumed for the effects. Current available choices are: Normal ('norm'), Beta ('beta'), Gamma ('gamma'), Exponential ('exp'), Weibull ('weib'), Logistic ('logis'), Poisson ('pois'), Negative Binomial ('negbin') or Bernoulli ('bern')
dist_c	Distribution assumed for the costs. Current available choices are: Normal ('norm'), Gamma ('gamma') or LogNormal ('lnorm')
model_txt_info	list containing model specification information used to write the txt file of the JAGS model.
model_string_jags	text file of the model.

Examples

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missingHE documentation built on March 19, 2026, 5:06 p.m.