missingHE is a R package, available on CRAN which is aimed at providing some useful tools to analysts in order to handle missing outcome data under a full Bayesian framework in economic evaluations. The package relies on the R package R2jags to implement Bayesian methods via the statistical software JAGS to obtain inferences using Markov Chain Monte Carlo (MCMC) methods. Different types of missing data models are implemented in the package, including selection models, pattern mixture models and hurdle models. A range of parametric distributions can be specified when modelling the typical outcomes in an trial-based economic evaluations, namely the effectiveness and cost variabels, while simultaneously incorporating different assumptions about the missingness mechanism, which allows to easily perform sensitvity analysis to a range of alternative missing data assumptions according to the modelling choices selected by the user.

missingHE also provides functions, taken and adapted from other R packages, to assess the results of each type of model, including summaries of the posterior distributions of each model parameter, range and imputations of the missing values, different types of model diagnostics to assess convergence of the algorithm, posterior predictive checks, model assessment measures based on the fit to the observed data, and a general summary of the economic evaluations, including the results from probabilistic sensitivity analyses which are automatically performed within a Bayesian modelling framework.

For example, the function plot, when applied to the output of a model fitted using missingHE, produces graphs which compare the observed and imputed values for both cost and benefit measures in each treatment group to detect possible concerns about the plausibility of the imputations.

Example of a graphical output from missingHE

More information, including new updates, about missingHE can be found at my GitHub repository.

Assistant Professor in Statistics