Ontology highlight
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.
SUBMITTER: Wang G
PROVIDER: S-EPMC8984094 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Wang Guoqiao G Liu Lei L Li Yan Y Aschenbrenner Andrew J AJ Bateman Randall J RJ Delmar Paul P Schneider Lon S LS Kennedy Richard E RE Cutter Gary R GR Xiong Chengjie C
Alzheimer's & dementia (New York, N. Y.) 20220405 1
<h4>Introduction</h4>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).<h4>Methods</h4>The proportional cLDA (PcLDA) and proportional MMRM (p ...[more]