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

TL;DR

Remiod is a new R package that enables reference-based multiple imputation for binary and ordinal outcomes in longitudinal studies with non-ignorable missing data, facilitating sensitivity analysis.

Contribution

This paper introduces remiod, the first software tool for controlled multiple imputation of categorical longitudinal data under non-ignorable missingness.

Findings

Remiod effectively performs imputation for binary and ordinal outcomes.

The package supports sensitivity analysis under different missing data assumptions.

Illustrative examples demonstrate its practical application.

Abstract

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