Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214504000001178
Title: Unbiased estimating equations from working correlation models for irregularly timed repeated measures
Authors: Wang, Y.-G. 
Carey, V.J.
Keywords: Bias
Correlation
Efficiency
Estimating function
Longitudinal data
Repeated measure
Issue Date: Sep-2004
Citation: Wang, Y.-G., Carey, V.J. (2004-09). Unbiased estimating equations from working correlation models for irregularly timed repeated measures. Journal of the American Statistical Association 99 (467) : 845-853. ScholarBank@NUS Repository. https://doi.org/10.1198/016214504000001178
Abstract: The method of generalized estimating equations (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation models for irregularly timed repeated measures to supplement and enhance GEE. The supplementary estimating equations are obtained by differentiation of the Cholesky decomposition of the working correlation, or as score equations for decoupled Gaussian pseudolikelihood. The estimating equations are solved with computational effort equivalent to that required for a first-order GEE. Full details and analytic expressions are developed for a generalized Markovian model that was evaluated through simulation. Large-sample "sandwich" standard errors for working correlation parameter estimates are derived and shown to have good performance. The proposed estimating functions are further illustrated in an analysis of repeated measures of pulmonary function in children.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105450
ISSN: 01621459
DOI: 10.1198/016214504000001178
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