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Health economic evaluations from individual level data with missing values using a set of pre-defined Bayesian models written in BUGS. A series of parametric models are available to jointly model partially-observed effectiveness and cost outcomes under both ignorable and nonignroable missing data mechanism assumptions

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README.md

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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 Markov Chain Monte Carlo (MCMC) methods. 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.

Installation

There are two ways of installing missingHE. A "stable" version is packaged and binary files are available for Windows and as source. To install the stable version on a Windows machine, run the following command

install.packages("missingHE")

which installs the package from a CRAN mirror and ensures that install.packages() can also install the "dependencies" (e.g. other packages that are required for missingHE to work).

It is also possible to install missingHE using the "development" version - this will usually be updated frequently and may be continuously tested. On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running

pkgs <- c("R2jags","ggplot2","gridExtra","BCEA","ggmcmc","loo","Rtools","devtools", "utils")
repos <- c("https://cran.rstudio.com") 
install.packages(pkgs,repos=repos,dependencies = "Depends")

before installing the package using devtools:

devtools::install_github("AnGabrio/missingHE", build_vignettes = TRUE)

The optional argument build_vignettes = TRUE allows to install the vignettes of the package locally on your computer. These consist in brief tutorials to guide the user on how to use and customise the models in missingHE using different functions of the package. Once the package is installed, they can be accessed using the command

utils::browseVignettes(package = "missingHE")

which shows all the vignettes available for the package.

All models implemented in missingHE are written in the BUGS language using the software JAGS, which needs to be installed from its own repository and instructions for installations under different OS can be found online. Once installed, the software is called in missingHE via the R package R2jags. Note that the missingHE package is currently under active development and therefore it is advisable to reinstall the package directly from GitHub before each use to ensure that you are using the most updated version.

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Health economic evaluations from individual level data with missing values using a set of pre-defined Bayesian models written in BUGS. A series of parametric models are available to jointly model partially-observed effectiveness and cost outcomes under both ignorable and nonignroable missing data mechanism assumptions

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