A comparative study of models for the different covariance structure analysis of repeated measurement data

作     者：Yazhou Wu Ling Zhang Liang Zhou Xiaoyu Liu Ling Liu Yanqi Zhang Dong Yi 

作者机构：Department of Health StatisticsThird Military Medical University Chongqing 400038 P. R. China 

出 版 物：《International Journal of Biomathematics》 (生物数学学报（英文版）)

年 卷 期：2017年第10卷第1期

页      面：115-125页

核心收录：

学科分类：0701[理学-数学] 070101[理学-基础数学] 

基　　金：National Natural Science Foundation of China [81573254, 81273178] Higher Education Scientific Research Subject of Higher Education Institute in Chongqing [CQGJ13C652] 

主　　题：Repeated measurement data generalized linear models mixed model covari-ance structure. 

摘      要：In repeated measurement data, the variables are not independent, and a certain auto- correlation typically exists between different levels of repeated measurement factors. The random error is composed of at least two parts, i.e. the individual random effect and the intra-individual multi-repeated measurement effect. Traditional statistical analysis methods （such as the t-test and the one-way analysis of variance） are not applicable. The linear mixed model has been widely applied for the analysis and design of repeated measurement data. This paper focuses on medical examples and describes the selection of a covariance structure for the linear mixed model of repeated measurement in the modeling of different variance-eovariance structures. By selecting different covariance structures, we can perform the parameter estimation and statistical test for the fixed effect of repeated measurement data, the parameters of random effects, and the covari- ance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.