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

Abstract

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 [Formula: see text]-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–covariance 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 covariance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.