A novel cognitive disease progression model for clinical trials in autosomal-dominant Alzheimer's disease

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.

Keywords: Alzheimer's disease; disease progression model; mixed effects model for repeated measures; proportional treatment effect.

Copyright © 2018 John Wiley & Sons, Ltd.

Figures

FIGURE 1

The decline trajectories for natural history and for 30% and 70% treatment effects [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Power comparison between the cognitive disease progression model (DPM) and mixed model for repeated measures (MMRM) for a sample size of 60:20 (treatment: placebo). The gains in power using the cognitive DPM are striking compared with the commonly used MMRM model [Colour figure can be viewed at wileyonlinelibrary.com]