Open AccessProportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease
Author(s) -
Wang Guoqiao,
Liu Lei,
Li Yan,
Aschenbrenner Andrew J.,
Bateman Randall J.,
Delmar Paul,
Schneider Lon S.,
Kennedy Richard E.,
Cutter Gary R.,
Xiong Chengjie
Publication year - 2022
Publication title -
alzheimer's and dementia: translational research and clinical interventions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.49
H-Index - 30
ISSN - 2352-8737
DOI - 10.1002/trc2.12286
Subject(s) - disease , longitudinal data , clinical trial , alzheimer's disease , medicine , psychology , computer science , data mining
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.