On inference of control-based imputation for analysis of repeated binary outcomes with missing data

Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.