{ "_id": "6a1dc1541d7bb097a0a587ce", "Package": "missingHE", "Type": "Package", "Title": "Missing Outcome Data in Health Economic Evaluation", "Version": "1.6.1", "Authors@R": "person(\"Andrea\", \"Gabrio\", email = \"a.gabrio@maastrichtuniversity.nl\", role = c(\"aut\", \"cre\"))", "Description": "Contains a suite of functions for health economic\nevaluations with missing outcome data. The package can fit\ndifferent types of statistical models under a fully Bayesian\napproach using the software 'JAGS' (which should be installed\nlocally and which is loaded in 'missingHE' via the 'R' package\n'R2jags'). Three classes of models can be fitted under a\nvariety of missing data assumptions: selection models, pattern\nmixture models and hurdle models. In addition to model fitting,\n'missingHE' provides a set of specialised functions to assess\nmodel convergence and fit, and to summarise the statistical and\neconomic results using different types of measures and graphs.\nThe methods implemented are described in Mason (2018)\n, Molenberghs (2000)\n and Gabrio (2019)\n.", "VignetteBuilder": "knitr", "License": "GPL-2", "Encoding": "UTF-8", "LazyData": "true", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake make jags libicu-dev libuv1-dev libssl-dev", "Repository": "https://angabrio.r-universe.dev", "Date/Publication": "2026-06-01 15:15:26 UTC", "RemoteUrl": "https://github.com/angabrio/missinghe", "RemoteRef": "HEAD", "RemoteSha": "1589944783da40765154e4b9e2d526350a55e84d", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-01 17:16:40 UTC", "User": "root" }, "Author": "Andrea Gabrio [aut, cre]", "Maintainer": "Andrea Gabrio ", "MD5sum": "402d44222debff5149ec68016029ba4c", "_user": "angabrio", "_type": "src", 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different ways", "topics": [ "data_read_lmdm" ] }, { "page": "data_read_pattern", "title": "A function to read and re-arrange the data in different ways", "topics": [ "data_read_pattern" ] }, { "page": "data_read_selection", "title": "A function to read and re-arrange the data in different ways", "topics": [ "data_read_selection" ] }, { "page": "diagnostic", "title": "Diagnostic checks for assessing MCMC convergence of Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'.", "topics": [ "diagnostic" ] }, { "page": "fb", "title": "An internal function to extract the random effects component from an object of class formula", "topics": [ "fb" ] }, { "page": "hurdle", "title": "Full Bayesian Models to handle missingness in Economic Evaluations (Hurdle Models)", "topics": [ "hurdle" ] }, { "page": "isAnyArgBar", "title": "An internal function to detect the random effects component from an object of class formula", "topics": [ "isAnyArgBar" ] }, { "page": "isBar", "title": "An internal function to detect the random effects component from an object of class formula", "topics": [ "isBar" ] }, { "page": "jagsresults", "title": "An internal function to summarise results from BUGS model", "topics": [ "jagsresults" ] }, { "page": "lmdm", "title": "Full Bayesian Models to handle missingness in Economic Evaluations (Longitudinal Missing Data Models)", "topics": [ "lmdm" ] }, { "page": "MenSS", "title": "MenSS economic data on STIs", "topics": [ "MenSS" ] }, { "page": "nobars_", "title": "An internal function to separate the fixed and random effects components from an object of class formula", "topics": [ "nobars_" ] }, { "page": "pattern", "title": "Full Bayesian Models to handle missingness in Economic Evaluations (Pattern Mixture Models)", "topics": [ "pattern" ] }, { "page": "PBS", "title": "PBS economic data on intellectual disability and challenging behaviour", "topics": [ "PBS" ] }, { "page": "pic", "title": "Predictive information criteria for Bayesian models fitted in 'JAGS' using the function 'selection', 'pattern', 'hurdle' or 'lmdm'", "topics": [ "pic" ] }, { "page": "plot.missingHE", "title": "Plot method for the imputed data contained in the objects of class 'missingHE'", "topics": [ "plot.missingHE" ] }, { "page": "ppc", "title": "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'", "topics": [ "ppc" ] }, { "page": "print.missingHE", "title": "Print method for the posterior results contained in the objects of class 'missingHE'", "topics": [ "print.missingHE" ] }, { "page": "prior_hurdle", "title": "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", "topics": [ "prior_hurdle" ] }, { "page": "prior_lmdm", "title": "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", "topics": [ "prior_lmdm" ] }, { "page": "prior_pattern", "title": "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 assumed", "topics": [ "prior_pattern" ] }, { "page": "prior_selection", "title": "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", "topics": [ "prior_selection" ] }, { "page": "run_hurdle", "title": "An internal function to execute a JAGS hurdle model and get posterior results", "topics": [ "run_hurdle" ] }, { "page": "run_lmdm", "title": "An internal function to execute a JAGS longitudinal missing data model and get posterior results", "topics": [ "run_lmdm" ] }, { "page": "run_pattern", "title": "An internal function to execute a JAGS pattern mixture model and get posterior results", "topics": [ "run_pattern" ] }, { "page": "run_selection", "title": "An internal function to execute a JAGS selection model and get posterior results", "topics": [ "run_selection" ] }, { "page": "selection", "title": "Full Bayesian Models to handle missingness in Economic Evaluations (Selection Models)", "topics": [ "selection" ] }, { "page": "summary.missingHE", "title": "Summary method for objects in the class 'missingHE'", "topics": [ "summary.missingHE" ] }, { "page": "write_hurdle", "title": "An internal function to select which type of hurdle model to execute. 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