Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

Leung, Denis H. Y., Wang, You-Gan, & Zhu, Min (2009) Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method. Biostatistics, 10(3), pp. 436-445.

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Abstract

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Impact and interest:

14 citations in Scopus
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11 citations in Web of Science®

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ID Code: 90454
Item Type: Journal Article
Refereed: Yes
Keywords: Empirical likelihood, Generalized estimating equations, Longitudinal, data, generalized estimating equations, empirical likelihood, clustered data, linear-models, binary data, regression
DOI: 10.1093/biostatistics/kxp002
ISSN: 1465-4644
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 02:51
Last Modified: 11 Dec 2015 02:49

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