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Biometrika 1999 86(2):459-465; doi:10.1093/biomet/86.2.459
© 1999 by Biometrika Trust
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Miscellanea. On the efficiency of regression estimators in generalised linear models for longitudinal data

BC SutradharA1 and K DasA2

A1 Department of Mathematics and Statistics, Memorial University of Newfoundland, St John's, NF, Canada A1C 5S7 E-mail: bsutradh@math.mun.ca A2 Department of Statistics, University of Calcutta, 35 Ballygunge Circular Road, Calcutta-700-019, India E-mail: kalyan@cubmb.ernet.in

Liang & Zeger (1986) introduced a generalised estimating equations approach based on a 'working' correlation matrix to obtain consistent and efficient estimators of regression parameters in the class of generalised linear models for repeated measures data. As demonstrated by Crowder (1995), because of the uncertainty of definition of the working correlation matrix, the Lian-Zeger approach may in some cases lead to a complete breakdown of the estimation of the regression parameters. In this paper we show that, even though the Lian-Zeger approach in many situations yields consistent estimators for the regression parameters, these estimators are usually inefficient as compared to the regression estimators obtained by using the independence estimating equations approach.

Key Words: Consistency; Efficiency; Generalised linear model; Repeated measures data; Robust correlation structure.


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