Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2009

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.

Keywords

Empirical likelihood, Generalized estimating equations, Longitudinal data

Discipline

Econometrics | Medicine and Health Sciences

Research Areas

Econometrics

Publication

Biostatistics

Volume

10

Issue

3

First Page

436

Last Page

445

ISSN

1465-4644

Identifier

10.1093/biostatistics/kxp002

Publisher

Oxford University Press

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1093/biostatistics/kxp002

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