Missing Outcome Data in Health Economic Evaluation

Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) , Molenberghs (2000) and Gabrio (2019) .


Reference manual

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1.4.1 by Andrea Gabrio, 3 months ago

Browse source code at https://github.com/cran/missingHE

Authors: Andrea Gabrio [aut, cre]

Documentation:   PDF Manual  

Task views: Missing Data

GPL-2 license

Imports mcmcplots, ggpubr, ggmcmc, ggthemes, BCEA, ggplot2, grid, gridExtra, bayesplot, methods, R2jags, loo, coda, mcmcr

Suggests knitr, rmarkdown

See at CRAN