Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease - Guoqiao Wang; Lei Liu; Yan Li; Andrew J. Aschenbrenner; Randall J. Bateman; Paul Delmar; Lon S. Schneider; Richard E. Kennedy; Gary R. Cutter; Chengjie Xiong (Alzheimer’s & Dementia: Translational Research & Clinical Interventions)
Abstract Introduction Clinical trials for sporadic Alzheimer's disease generally use mixed models for repeated measures (MMRM) or, to a lesser degree, constrained longitudinal data analysis models (cLDA) as the analysis model with time since baseline as a categorical variable. Inferences using MMRM/cLDA focus on the between‐group contrast at the pre‐determined, end‐of‐study assessments, thus are less efficient (eg, less power). Methods The proportional cLDA (PcLDA) and proportional MMRM (pMMRM) with time as a categorical variable are proposed to use all the post‐baseline data without the linearity assumption on disease progression. Results Compared with the traditional cLDA/MMRM models, PcLDA or pMMRM lead to greater gain in power (up to 20% to 30%) while maintaining type I error control. Discussion The PcLDA framework offers a variety of possibilities to model longitudinal data such as proportional MMRM (pMMRM) and two‐part pMMRM which can model heterogeneous cohorts more efficiently and model co‐primary endpoints simultaneously.