Generally Applicable Modes of Analyses for Incomplete Binary Longitudinal Clinical Trial Data

• Activity Number: 156
• Type: Contributed
• Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Ivy Jansen*+ and Caroline Beunckens and Geert Molenberghs and Geert Verbeke and Craig H. Mallinckrodt
• Companies: Limburgs Universitair Centrum and Limburgs Universitair Centrum and Limburgs Universitair Centrum and Katholieke Universiteit Leuven and Eli Lilly and Company
• Keywords: complete case analysis ; generalized estimating equations ; generalized linear mixed models ; last observation carried forward ; missing at random

Abstract

Many clinical trials result in incomplete longitudinal data. Common analysis methods are complete case (CC) and last observation carried forward (LOCF), resting on strong and unrealistic assumptions. Many full longitudinal methods, valid under MAR, have been developed. We focus on non-Gaussian outcomes, a setting more complicated than the Gaussian counterpart, due to the lack of an analogy for the linear mixed model. Model choices include the random-effects based generalized linear mixed models (GLMM) and the marginal generalized estimating equations (GEE). Since the latter is non-likelihood-based, it requires modification (weighted GEE) to be valid under MAR. Both methods provide similar results for hypothesis testing, but the estimated parameters have different interpretation. Current statistical computing brings GLMM and WGEE within reach and their implementation in depression trials is presented, showing they are viable alternatives for CC and LOCF, even when a single time point only (e.g., the last) is of interest. Even then, all information from all profiles, complete and incomplete, is used, showing this approach is fully compatible with the intention-to-treat principle.