Working covariance model selection for generalized estimating equations

Carey, Vincent J. & Wang, You-Gan (2011) Working covariance model selection for generalized estimating equations. Statistics in Medicine, 30(26), pp. 3117-3124.

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We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice.

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7 citations in Scopus
8 citations in Web of Science®
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ID Code: 90445
Item Type: Journal Article
Refereed: Yes
Keywords: pseudolikelihood, correlation, covariance models, estimating functions, longitudinal data, repeated measures, longitudinal count data, linear-models, binary data, parameters, regression, overdispersion, responses, tests
DOI: 10.1002/sim.4300
ISSN: 0277-6715
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Nov 2015 03:18
Last Modified: 26 Jun 2017 09:01

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