A novel cognitive disease progression model for clinical trials in autosomal‐dominant Alzheimer's disease. (14th May 2018)

• Record Type: Journal Article
• Authors: Wang, Guoqiao; Berry, Scott; Xiong, Chengjie; Hassenstab, Jason; Quintana, Melanie; McDade, Eric M.; Delmar, Paul; Vestrucci, Matteo; Sethuraman, Gopalan; Bateman, Randall J.
• Is Part Of: Statistics in medicine. Volume 37:Number 21(2018)
• Page Start: 3047
• Page End: 3055
• Publication Date: 2018-05-14
• Subjects: Alzheimer's disease -- disease progression model -- mixed effects model for repeated measures -- proportional treatment effect; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7811
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Clinical trial outcomes for Alzheimer's disease are typically analyzed by using the mixed model for repeated measures (MMRM) or similar models that compare an efficacy scale change from baseline between treatment arms with or without participants' disease stage as a covariate. The MMRM focuses on a single‐point fixed follow‐up duration regardless of the exposure for each participant. In contrast to these typical models, we have developed a novel semiparametric cognitive disease progression model (DPM) for autosomal dominant Alzheimer's disease based on the Dominantly Inherited Alzheimer Network (DIAN) observational study. This model includes 3 novel features, in which the DPM (1) aligns and compares participants by disease stage, (2) uses a proportional treatment effect similar to the concept of the Cox proportional hazard ratio, and (3) incorporates extended follow‐up data from participants with different follow‐up durations using all data until last participant visit. We present the DPM model developed by using the DIAN observational study data and demonstrate through simulation that the cognitive DPM used in hypothetical intervention clinical trials produces substantial gains in power compared with the MMRM.