Mixed effect models of longitudinal Alzheimer's disease data: a cautionary note

作     者：Milliken, JK Edland, SD 

作者机构：Univ Washington Dept Epidemiol Seattle WA 98195 USA 

出 版 物：《STATISTICS IN MEDICINE》 (医学统计学)

年 卷 期：2000年第19卷第11-12期

页      面：1617-1629页

核心收录：

学科分类：0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0714[理学-统计学（可授理学、经济学学位）] 10[医学] 

基　　金：NIA NIH HHS [AG06781, AG05136] Funding Source: Medline NIEHS NIH HHS [5 T32 ES07262-08] Funding Source: Medline 

主　　题：阿尔茨海默病/诊断 阿尔茨海默病/流行病学 数据收集/统计学和数值数据 疾病恶化 纵向研究 模型 统计学 神经心理学测验/统计学和数值数据 心理测定学 老年人 人类 

摘      要：Longitudinal studies of cognitive function in Alzheimer s disease (AD) patients are powerful tools to better understand the biology and natural history of the disease, but the attributes of the studies that make them valuable also pose special challenges to analysts. A fundamental problem is the accurate measure of time at which cognitive decline begins. Investigators typically use the date of AD diagnosis or the date of enrolment in an AD study. If the rate of cognitive decline is non-linear, variables associated with the time of diagnosis or enrolment might artificially be associated with the rate of decline. Unlike the mixed effects models typically used to analyse cognitive decline, summary measure analyses do not directly compare the rate of decline with time since decline began, and, therefore, are less sensitive to biased measures of time of decline. We simulated trajectories of cognitive decline using the multivariate normal random effect model and tested the ability of the two analytic techniques to discriminate between true and spurious associations. Our analyses suggest summary measure models are less likely to detect spurious associations generated by biased measures of time at which decline begins, and more likely to detect true associations concealed by biased time measurement. Copyright (C) 2000 John Wiley & Sons, Ltd.