run_lmdm: An internal function to execute a JAGS longitudinal missing...

In missingHE: Missing Outcome Data in Health Economic Evaluation

An internal function to execute a JAGS longitudinal missing data model and get posterior results

Description

This function fits a JAGS using the jags function and obtain posterior inferences.

Usage

run_lmdm(data_model, type, dist_e, dist_c, model_info)

Arguments

data_model	list containing the data for the model to be passed to JAGS.
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).
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_info	list containing model and MCMC information to be passed to JAGS.

Examples

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