EVALUATION OF STATISTICAL METHODS FOR ANALYSIS OF SMALL-SAMPLE LONGITUDINAL CLINICAL TRIALS WITH DROPOUTS

Abstract

In longitudinal clinical trials that compare treatments of chronic diseases missing data occur mainly because of dropouts, where patients stop participating in the trial before the completion due to various reasons. Such incomplete data are often analyzed by using so-called completer analysis and/or LOCF (Last Observation Carried Forward). However, such procedures require strong assumptions for their validity. Multiple imputation (MI) (Rubin, 1987) is a valid method under MAR (Missing At Random). This method consists of three steps ("imputation", "analysis" and "combination") and various methods for MI also have been proposed. In this paper, we evaluate the performance of four methods for MI contrasted with completer analysis and LOCF via Monte-Carlo simulations in the context of small-sample longitudinal clinical trials for comparison of two treatments. The performance of these methods with non-normal data (i.e. mixture of responders and non-responders) is also examined.