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Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.


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

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Publications

Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.

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]

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