Reference-based imputation (RBI) methods have been proposed as sensitivity analyses for longitudinal clinical trials with missing data. The RBI methods multiply impute the missing data in treatment group based on an imputation model built using data from the reference (control) group. The RBI will yield a conservative treatment effect estimate as compared to the estimate obtained from multiple imputation (MI) under missing at random (MAR). However, the RBI analysis based on the regular MI approach can be overly conservative because it not only applies discount to treatment effect estimate but also posts penalty on the variance estimate. In this article, we investigate the statistical properties of RBI methods, and propose approaches to derive accurate variance estimates using both frequentist and Bayesian methods for the RBI analysis. Results from simulation studies and applications to longitudinal clinical trial datasets are presented.

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