Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/90.1.29
Title: Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance
Authors: Wang, Y.-G. 
Carey, V.
Keywords: Design matrix
Efficiency
Estimating function
Longitudinal data
Pseudolikelihood
Repeated measures
Issue Date: Mar-2003
Citation: Wang, Y.-G., Carey, V. (2003-03). Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance. Biometrika 90 (1) : 29-41. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/90.1.29
Abstract: 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.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105467
ISSN: 00063444
DOI: 10.1093/biomet/90.1.29
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