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Biometrika 2003 90(1):29-41; doi:10.1093/biomet/90.1.29
© 2003 by Biometrika Trust
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Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance

You-Gan Wang1 and Vincent Carey2

1 Department of Statistics and Applied Probability, National University of Singapore, Singapore 117543 stawyg{at}nus.edu.sg 2 Channing Laboratory, Harvard Medical School, 181 Longwood Avenue, Boston, Massachusetts 02115, U.S.A.channing.harvard.edu

The method of generalised estimating equations for regression modelling of clustered outcomes allows for specification of a working matrix that is intended to approximate the true correlation matrix of the observations.We investigate the asymptotic relative efficiency of the generalised estimating equation for the mean parameters when the correlation parameters are estimated by various methods. The asymptotic relative efficiency depends on three features of the analysis, namely (i) the discrepancy between the working correlation structure and the unobservable true correlation structure, (ii) the method by which the correlation parameters are estimated and (iii) the ‘design’, by which we refer to both the structures of the predictor matrices within clusters and distribution of cluster sizes. Analytical and numerical studies of realistic data-analysis scenarios show that choice of working covariance model has a substantial impact on regression estimator efficiency. Protection against avoidable loss of efficiency associated with covariance misspecification is obtained when a ‘Gaussian estimation’ pseudolikelihood procedure is used with an AR(1) structure.

Key Words: Design matrix; Efficiency; Estimating function; Longitudinal data; Pseudolikelihood; Repeated measures

Received November 2000. Revised April 2002


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