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Missing data on response variables are common in clinical studies.Corresponding to the uncertainty of missing mechanism, theoretical frameworkson controlled imputation have been developed. In practice, it is recommended toconduct a statistically valid analysis under the primary assumptions on missingdata, followed by sensitivity analysis under alternative assumptions to assessthe robustness of results. Due to the availability of software, controlledmultiple imputation (MI) procedures, including delta-based and reference-basedapproaches, have become popular for analyzing continuous variables undermissing-not-at-random assumptions. Similar tools, however, still limitapplication of these methods to categorical data. In this paper, we introducethe R package \textbf{remiod}, which utilizes the Bayesian framework to performimputation in regression models on binary and ordinal outcomes. Followingoutlining theoretical backgrounds, usage and features of \textbf{remiod} aredescribed and illustrated using examples.

Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness