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 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) .


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install.packages("missingHE")

1.1.1 by Andrea Gabrio, a month ago


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


Authors: Andrea Gabrio [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


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


See at CRAN