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- Title
Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.
- Authors
Guoqiao Wang; Lei Liu; Yan Li; Aschenbrenner, Andrew J.; Bateman, Randall J.; Delmar, Paul; Schneider, Lon S.; Kennedy, Richard E.; Cutter, Gary R.; Chengjie Xiong
- Abstract
Introduction: Clinical trials for sporadic Alzheimer's disease generally usemixed models for repeatedmeasures (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: Comparedwith the traditional cLDA/MMRMmodels, PcLDA orpMMRMlead 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.
- Subjects
ALZHEIMER'S disease; PANEL analysis; DATA analysis; DATA modeling; FALSE positive error
- Publication
Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2022, Vol 8, Issue 1, p1
- ISSN
2352-8737
- Publication type
Article
- DOI
10.1002/trc2.12286