Package: missingHE 1.6.1

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>.

Authors:Andrea Gabrio [aut, cre]

missingHE_1.6.1.tar.gz
missingHE_1.6.1.zip(r-4.7)missingHE_1.6.1.zip(r-4.6)missingHE_1.6.1.zip(r-4.5)
missingHE_1.6.1.tgz(r-4.6-any)missingHE_1.6.1.tgz(r-4.5-any)
missingHE_1.6.1.tar.gz(r-4.7-any)missingHE_1.6.1.tar.gz(r-4.6-any)
missingHE_1.6.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
missingHE/json (API)

# Install 'missingHE' in R:
install.packages('missingHE', repos = c('https://angabrio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/angabrio/missinghe/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3
Datasets:
  • MenSS - MenSS economic data on STIs
  • PBS - PBS economic data on intellectual disability and challenging behaviour

On CRAN:

Conda:

cost-effectiveness-analysishealth-economic-evaluationindividual-level-datajagsmissing-dataparametric-modellingsensitivity-analysiscpp

6.27 score 6 stars 31 scripts 551 downloads 12 exports 148 dependencies

Last updated from:1589944783. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK264
source / vignettesOK480
linux-release-x86_64OK257
macos-release-arm64OK240
macos-oldrel-arm64OK223
windows-develOK194
windows-releaseOK196
windows-oldrelOK178
wasm-releaseOK165

Exports:data_read_hurdledata_read_lmdmdata_read_patterndata_read_selectiondiagnostichurdlejagsresultslmdmpatternpicppcselection

Dependencies:abindaskpassbackportsbase64encbayesplotBCEAbootbroombslibcachemcarcarDatacheckmatechkclicodacolorspacecorrplotcowplotcpp11crayoncrosstalkcurldata.tabledbartsDerivdigestdistributionaldoBydplyrearthevaluateextrasfarverfastmapfontawesomeforcatsforecastFormulafracdifffsgenericsGGallyggmcmcggplot2ggpubrggrepelggridgesggsciggsignifggstatsggthemesgluegridExtragtablehighrhmshtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4lmtestloomagrittrMASSMatrixMatrixModelsmatrixStatsmcmcrmemoisemgcvmicrobenchmarkmimeminqamodelrmvtnormnlistnlmenloptrnnetnumDerivopensslotelpatchworkpbkrtestpillarpkgconfigplotlyplotmoplotrixplyrpolynomposteriorprettyunitsprogresspromisespurrrquantregR2jagsR2WinBUGSR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rjagsrlangrmarkdownrstatixS7sassscalesSparseMstringistringrsurvivalsystensorAtermtibbletidyrtidyselecttimeDatetinytexuniversalsurcautf8vctrsviridisLitevoiwithrxfunyamlzoo

Introduction to missingHE
Modelling Framework | Missingness Approach | Selection Models | Pattern Mixture Models | Hurdle Models | The MenSS trial | Model fitting | Fitting models under MAR | Model assessment | Convergence diagnostics | Posterior predictive checks | Predictive information criteria | Results inspection | Summarise parameter estimates | Inspect imputed values | Summarise health economic results | Probabilistic sensitivity analysis
Rendered fromIntroduction_to_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-06-01
Started: 2020-05-22

Fitting MNAR models in missingHE
Handling MNAR using selection models | Handling MNAR using pattern mixture models | Handling MNAR using hurdle models
Rendered fromFitting_MNAR_models_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-03-19
Started: 2020-05-24

Longitudinal models in missingHE
Modelling Framework | Missingness Approach | The PBS trial | Model fitting | Fitting models under MAR | Model assessment | Convergence diagnostics | Posterior predictive checks | Predictive information criteria | Results inspection | Summarise parameter estimates | Inspect imputed values | Summarise health economic results | Probabilistic sensitivity analysis
Rendered fromLongitudinal_models_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-03-19
Started: 2023-03-20

Model and output customisation in missingHE
Modelling assumptions | Outcome distribution | Missingness mechanism | Outcome model structure | Prior distributions | Conclusions
Rendered fromModel_customisation_in_missingHE.Rmdusingknitr::rmarkdown

Last update: 2026-03-19
Started: 2020-05-24

Readme and manuals

Help Manual

Help pageTopics
An internal function to detect the random effects component from an object of class formulaanyBars
Extract regression coefficient estimates from objects in the class 'missingHE'coef.missingHE
A function to read and re-arrange the data in different ways for the hurdle modeldata_read_hurdle
A function to read and re-arrange the data in different waysdata_read_lmdm
A function to read and re-arrange the data in different waysdata_read_pattern
A function to read and re-arrange the data in different waysdata_read_selection
Diagnostic checks for assessing MCMC convergence of Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'.diagnostic
An internal function to extract the random effects component from an object of class formulafb
Full Bayesian Models to handle missingness in Economic Evaluations (Hurdle Models)hurdle
An internal function to detect the random effects component from an object of class formulaisAnyArgBar
An internal function to detect the random effects component from an object of class formulaisBar
An internal function to summarise results from BUGS modeljagsresults
Full Bayesian Models to handle missingness in Economic Evaluations (Longitudinal Missing Data Models)lmdm
MenSS economic data on STIsMenSS
An internal function to separate the fixed and random effects components from an object of class formulanobars_
Full Bayesian Models to handle missingness in Economic Evaluations (Pattern Mixture Models)pattern
PBS economic data on intellectual disability and challenging behaviourPBS
Predictive information criteria for Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'pic
Plot method for the imputed data contained in the objects of class 'missingHE'plot.missingHE
Posterior predictive checks for assessing the fit to the observed data of Bayesian models implemented in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'ppc
Print method for the posterior results contained in the objects of class 'missingHE'print.missingHE
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 assumedprior_hurdle
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 assumedprior_lmdm
An internal function to change the hyperprior parameters in the pattern mixture model provided by the user depending on the type of missingness mechanism and outcome distributions assumedprior_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 assumedprior_selection
An internal function to execute a JAGS hurdle model and get posterior resultsrun_hurdle
An internal function to execute a JAGS longitudinal missing data model and get posterior resultsrun_lmdm
An internal function to execute a JAGS pattern mixture model and get posterior resultsrun_pattern
An internal function to execute a JAGS selection model and get posterior resultsrun_selection
Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)selection
Summary method for objects in the class 'missingHE'summary.missingHE
An internal function to select which type of hurdle model to execute. 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_hurdle
An internal function to select which type of longitudinal missing data 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.write_lmdm
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.write_pattern
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.write_selection