Working-correlation-structure identification in generalized estimating equations

Hin, Lin-Yee & Wang, You-Gan (2009) Working-correlation-structure identification in generalized estimating equations. Statistics in Medicine, 28(4), pp. 642-658.

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Abstract

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well-known criterion of QIC for selecting a working correlation Structure. and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads LIS to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study.

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42 citations in Web of Science®

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ID Code: 90455
Item Type: Journal Article
Refereed: Yes
Keywords: clustered data, correlation modelling, correlation information, criterion, covariance, efficiency, generalized estimating equations, model selection, QIC, working correlation structure, longitudinal data, linear-models, gee analyses, selection, misspecification, error
DOI: 10.1002/sim.3489
ISSN: 0277-6715
Deposited On: 17 Nov 2015 02:55
Last Modified: 11 Dec 2015 03:02

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