Reference-based multiple imputation for sensitivity analysis of clinical trials with missing data

The statistical analysis of longitudinal randomized clinical trials is frequently complicated by the occurrence of protocol deviations that result in incomplete datasets for analysis. However one approaches analysis, an untestable assumption about the distribution of the unobserved postdeviation data must be made. In such circumstances, it is important to assess the robustness of trial results from primary analysis to different credible assumptions about the distribution of the unobserved data. Reference-based multiple-imputation procedures allow trialists to assess the impact of contextually relevant qualitative missing data assumptions (Carpenter, Roger, and Kenward 2013). For example, in a trial of an active versus placebo treatment, missing data for active patients can be imputed following the distribution of the data in the placebo arm. I present the mimix command, which implements the reference-based multiple-imputation procedures in Stata, enabling relevant accessible sensitivity analysis of trial datasets.