missingHE: 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) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

Version: 1.4.1
Depends: R (≥ 3.5.0)
Imports: mcmcplots, ggpubr, ggmcmc, ggthemes, BCEA, ggplot2, grid, gridExtra, bayesplot, methods, R2jags, loo, coda, mcmcr
Suggests: knitr, rmarkdown
Published: 2020-06-25
Author: Andrea Gabrio [aut, cre]
Maintainer: Andrea Gabrio <ucakgab at ucl.ac.uk>
License: GPL-2
NeedsCompilation: no
Materials: README
In views: MissingData
CRAN checks: missingHE results


Reference manual: missingHE.pdf
Vignettes: Fitting MNAR models in missingHE
Introduction to missingHE
Model Customisation in missingHE
Package source: missingHE_1.4.1.tar.gz
Windows binaries: r-devel: missingHE_1.4.1.zip, r-release: missingHE_1.4.1.zip, r-oldrel: missingHE_1.4.1.zip
macOS binaries: r-release: missingHE_1.4.1.tgz, r-oldrel: missingHE_1.4.1.tgz
Old sources: missingHE archive


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