Methods for Missing Data Handling in Randomized Clinical Trials with Non-Normal Endpoints with Application to a Phase III Rheumatoid Arthritis Clinical Trial

• Activity Number: 395
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Chunpeng Fan*+ and Donghui Zhang and Lynn Wei and Gary Koch
• Companies: Sanofi and Sanofi and Sanofi and University of North Carolina at Chapel Hill
• Keywords: MAR ; Missing at random ; MNAR ; Missing not at random ; Nonparametric ; Rank

Abstract

In randomized clinical trials, when the endpoint is change from baseline at last scheduled visit, various parametric, semiparametric, and nonparametric methods have been developed to handle the possible missing data due to dropouts. Although last observation carried forward (LOCF) and mixed model for repeated measured (MMRM) have been extensively compared and widely used, they may lead to biased results when the required distributional or missing mechanism assumptions are not satisfied. Nonparametric missing data handling methods including the last rank carried forward (LRCF) and mean rank imputation (MRI) relax the underlying distributional assumption; however, conditions for them to be valid have been investigated to a very limited extent. This paper rigorously derives asymptotic properties of the mean rank imputation method and proves its validity to test the primary endpoint under certain mild distributional and missing mechanism assumptions. Test-based location difference between the treatment and control groups is also derived when the randomized clinical trial has two arms under a location-shift assumption. The investigated methods are applied to a recent phase III clinical