Working covariance model selection for generalized estimating equations

Carey, Vincent J. and Wang, You-Gan (2011) Working covariance model selection for generalized estimating equations. Statistics in Medicine, 30 26: 3117-3124. doi:10.1002/sim.4300

Author Carey, Vincent J.
Wang, You-Gan
Title Working covariance model selection for generalized estimating equations
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
Publication date 2011-11
Sub-type Article (original research)
DOI 10.1002/sim.4300
Volume 30
Issue 26
Start page 3117
End page 3124
Total pages 8
Place of publication Bognor Regis, West Sussex, United Kingdom
Publisher John Wiley & Sons
Collection year 2012
Language eng
Abstract 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.
Keyword Pseudolikelihood
Covariance models
Estimating functions
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2012 Collection
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 7 times in Scopus Article | Citations
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