散发性阿尔茨海默病临床试验的比例约束纵向数据分析模型
Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease.
作者信息
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
机构信息
Division of Biostatistics Washington University School of Medicine St. Louis Missouri USA.
Department of Neurology Washington University School of Medicine St. Louis Missouri USA.
出版信息
Alzheimers Dement (N Y). 2022 Apr 5;8(1):e12286. doi: 10.1002/trc2.12286. eCollection 2022.
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.
引言
散发性阿尔茨海默病的临床试验通常使用重复测量混合模型（MMRM），或者在较小程度上使用受限纵向数据分析模型（cLDA）作为分析模型，将自基线以来的时间作为分类变量。使用MMRM/cLDA进行的推断聚焦于预定的研究结束时评估中的组间对比，因此效率较低（例如，检验效能较低）。
方法
提出了将时间作为分类变量的比例cLDA（PcLDA）和比例MMRM（pMMRM），以使用所有基线后的数据，而无需对疾病进展做线性假设。
结果
与传统的cLDA/MMRM模型相比，PcLDA或pMMRM在保持I型错误控制的同时，检验效能有更大提高（高达20%至30%）。
讨论
PcLDA框架为纵向数据建模提供了多种可能性，如比例MMRM（pMMRM）和两部分pMMRM，它们可以更有效地对异质性队列进行建模，并同时对共同主要终点进行建模。
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