Goodness-of-fit measures of R2 for repeated measures mixed effect models

Honghu Liu, Yan Zheng and Jie Shen

Journal of Applied Statistics, 2008, vol. 35, issue 10, 1081-1092

Abstract: Linear mixed effects model (LMEM) is efficient in modeling repeated measures longitudinal data. However, little research has been done in developing goodness-of-fit measures that can evaluate the models, particularly those that can be interpreted in an absolute sense without referencing a null model. This paper proposes three coefficient of determination (R2) as goodness-of-fit measures for LMEM with repeated measures longitudinal data. Theorems are presented describing the properties of R2 and relationships between the R2 statistics. A simulation study was conducted to evaluate and compare the R2 along with other criteria from literature. Finally, we applied the proposed R2 to a real virologic response data of an HIV-patient cohort. We conclude that our proposed R2 statistics have more advantages than other goodness-of-fit measures in the literature, in terms of robustness to sample size, intuitive interpretation, well-defined range, and unnecessary to determine a null model.

Keywords: repeated measures; R-square; linear mixed effects model; fixed effects; random effects; simulation (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/02664760802124422

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