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Goodness-of-fit measures of R 2 for repeated measures mixed effect models

Goodness-of-fit measures of R 2 for repeated measures mixed effect models 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 (R 2) as goodness-of-fit measures for LMEM with repeated measures longitudinal data. Theorems are presented describing the properties of R 2 and relationships between the R 2 statistics. A simulation study was conducted to evaluate and compare the R 2 along with other criteria from literature. Finally, we applied the proposed R 2 to a real virologic response data of an HIV-patient cohort. We conclude that our proposed R 2 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Statistics Taylor & Francis

Goodness-of-fit measures of R 2 for repeated measures mixed effect models

Liu, Honghu; Zheng, Yan; Shen, Jie
Journal of Applied Statistics , Volume 35 (10): 12 – Oct 1, 2008
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References (22)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1360-0532
eISSN
0266-4763
DOI
10.1080/02664760802124422
Publisher site
See Article on Publisher Site

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 (R 2) as goodness-of-fit measures for LMEM with repeated measures longitudinal data. Theorems are presented describing the properties of R 2 and relationships between the R 2 statistics. A simulation study was conducted to evaluate and compare the R 2 along with other criteria from literature. Finally, we applied the proposed R 2 to a real virologic response data of an HIV-patient cohort. We conclude that our proposed R 2 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.

Journal

Journal of Applied Statistics – Taylor & Francis

Published: Oct 1, 2008

Keywords: repeated measures; R-square; linear mixed effects model; fixed effects; random effects; simulation

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