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Biostatistics Advance Access originally published online on April 4, 2009
Biostatistics 2009 10(3):436-445; doi:10.1093/biostatistics/kxp002
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

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

Denis H. Y. Leung*

School of Economics, Singapore Management University, 90 Stamford Road, Singapore
denisleung{at}smu.edu.sg

You-Gan Wang

Commonwealth Scientific and Industrial Research Organization, Mathematical and Information Sciences, CSIRO Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia

Min Zhu

Finance Discipline, School of Business and Economics, University of Sydney, NSW 2006, Division of Mathematical and Information Sciences, Commonwealth Scientific Industrial Research Organisation, PO Box 120, Cleveland, QLD 4163, Australia

* To whom correspondence should be addressed.

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.

Keywords: Empirical likelihood; Generalized estimating equations; Longitudinal data

Received November 1, 2007; revised May 12, 2008; revised October 18, 2008; accepted for publication January 20, 2009.


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