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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 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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Version

Install

install.packages('missingHE')

Monthly Downloads

235

Version

1.4.1

License

GPL-2

Maintainer

Andrea Gabrio

Last Published

June 25th, 2020

Functions in missingHE (1.4.1)

MenSS

MenSS economic data on STIs
coef.missingHE

Extract regression coefficient estimates from objects in the class missingHE
diagnostic

Diagnostic checks for assessing MCMC convergence of Bayesian models fitted in JAGS using the function selection, pattern or hurdle
data_read_hurdle

A function to read and re-arrange the data in different ways for the hurdle model
data_read_pattern

A function to read and re-arrange the data in different ways
hurdle

Full Bayesian Models to handle missingness in Economic Evaluations (Hurdle Models)
anyBars

An internal function to detect the random effects component from an object of class formula
fb

An internal function to extract the random effects component from an object of class formula
data_read_selection

A function to read and re-arrange the data in different ways
isAnyArgBar

An internal function to detect the random effects component from an object of class formula
nobars_

An internal function to separate the fixed and random effects components from an object of class formula
plot.missingHE

Plot method for the imputed data contained in the objects of class missingHE
pic

Predictive information criteria for Bayesian models fitted in JAGS using the funciton selection, pattern or hurdle
prior_hurdle

An internal function to change the hyperprior parameters in the hurdle model provided by the user depending on the type of structural value mechanism and outcome distributions assumed
prior_pattern

An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed
run_hurdle

An internal function to execute a JAGS hurdle model and get posterior results
prior_selection

An internal function to change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumed
ppc

Posterior predictive checks for assessing the fit to the observed data of Bayesian models implemented in JAGS using the function selection, pattern or hurdle
print.missingHE

Print method for the posterior results contained in the objects of class missingHE
summary.missingHE

Summary method for objects in the class missingHE
selection

Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)
pattern

Full Bayesian Models to handle missingness in Economic Evaluations (Pattern Mixture Models)
jagsresults

An internal function to summarise results from BUGS model
run_selection

An internal function to execute a JAGS selection model and get posterior results
run_pattern

An internal function to execute a JAGS pattern mixture model and get posterior results
write_selection

An internal function to select which type of selection model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.
isBar

An internal function to detect the random effects component from an object of class formula
write_hurdle

An internal function to select which type of hurdle model to execute for both effectiveness and costs. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of structural value mechanism assumed and independence or joint modelling This function selects which type of model to execute.
write_pattern

An internal function to select which type of pattern mixture model to execute. Alternatives vary depending on the type of distribution assumed for the effect and cost variables, type of missingness mechanism assumed and independence or joint modelling This function selects which type of model to execute.