Package: missingHE 1.5.0

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]

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missingHE.pdf |missingHE.html✨
missingHE/json (API)

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

Peer review:

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

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • 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:

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

5.40 score 5 stars 25 scripts 275 downloads 12 exports 128 dependencies

Last updated 2 years agofrom:54a075d4b0. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 09 2024
R-4.5-winNOTENov 09 2024
R-4.5-linuxNOTENov 09 2024
R-4.4-winNOTENov 09 2024
R-4.4-macNOTENov 09 2024
R-4.3-winNOTENov 09 2024
R-4.3-macNOTENov 09 2024

Exports:data_read_hurdledata_read_patterndata_read_selectiondata_read_selection_longdiagnostichurdlejagsresultspatternpicppcselectionselection_long

Dependencies:abindbackportsbayesplotBCEABHbootbriobroomcallrcarcarDatacheckmatechkclicodacolorspacecorrplotcowplotcpp11crayondenstripDerivdescdiffobjdigestdistributionaldoBydplyrevaluateextrasfansifarverforcatsFormulafsgenericsGGallyggmcmcggplot2ggpubrggrepelggridgesggsciggsignifggstatsggthemesgluegridExtragtablehmsinlineisobandjsonlitelabelinglatticelifecyclelme4loomagrittrMASSMatrixMatrixModelsmatrixStatsmcmcplotsmcmcrMCMCvismgcvmicrobenchmarkminqamodelrmunsellnlistnlmenloptrnnetnumDerivoverlappingpatchworkpbkrtestpillarpkgbuildpkgconfigpkgloadplyrpolynomposteriorpraiseprettyunitsprocessxprogresspspurrrquantregQuickJSRR2jagsR2WinBUGSR6rbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreshape2rjagsrlangrprojrootrstanrstatixscalessfsmiscSparseMStanHeadersstringistringrsurvivaltensorAtermtestthattibbletidyrtidyselectuniversalsutf8vctrsviridisLitewaldowithr

Fitting MNAR models in missingHE

Rendered fromFitting_MNAR_models_in_missingHE.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-03-20
Started: 2020-05-24

Introduction to missingHE

Rendered fromIntroduction_to_missingHE.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-03-20
Started: 2020-05-22

Longitudinal Models in missingHE

Rendered fromLongitudinal_models_in_missingHE.Rmdusingknitr::rmarkdownon Nov 09 2024.

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

Model Customisation in missingHE

Rendered fromModel_customisation_in_missingHE.Rmdusingknitr::rmarkdownon Nov 09 2024.

Last update: 2023-03-20
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_pattern
A function to read and re-arrange the data in different waysdata_read_selection
A function to read and re-arrange the data in different waysdata_read_selection_long
Diagnostic checks for assessing MCMC convergence of Bayesian models fitted in 'JAGS' using the function 'selection', 'selection_long', 'pattern' or 'hurdle'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
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 funciton 'selection', 'selection_long', 'pattern' or 'hurdle'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', 'selection_long', 'pattern' or 'hurdle'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_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 change the hyperprior parameters in the selection model provided by the user depending on the type of missingness mechanism and outcome distributions assumedprior_selection_long
An internal function to execute a JAGS hurdle model and get posterior resultsrun_hurdle
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
An internal function to execute a JAGS selection model and get posterior resultsrun_selection_long
Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)selection
Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)selection_long
Summary method for objects in the class 'missingHE'summary.missingHE
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_hurdle
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
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_long