Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates-application to modeling the health of Filipino children

Leung, Denis Heng-Yan, Small, Dylan S., Qin, Jing, & Zhu, Min (2013) Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates-application to modeling the health of Filipino children. Biometrics, 69(3), pp. 624-632.

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Abstract

The method of generalized estimating equations (GEE) is a popular tool for analysing longitudinal (panel) data. Often, the covariates collected are time-dependent in nature, for example, age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Anderson (1994, Communications in Statistics, Simulations and Computation 23, 939–951) advocated using an independence working correlation assumption in the GEE model as a robust approach. However, using GEE with the independence correlation assumption may lead to significant efficiency loss (Fitzmaurice, 1995, Biometrics 51, 309–317). In this article, we propose a method that extracts additional information from the estimating equations that are excluded by the independence assumption. The method always includes the estimating equations under the independence assumption and the contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries. We apply the method to a longitudinal study of the health of a group of Filipino children.

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ID Code: 65311
Item Type: Journal Article
Refereed: Yes
Keywords: Empirical likelihood, Estimating functions, Generalized estimating equations, Longitudinal data
DOI: 10.1111/biom.12039
ISSN: 0006341X
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2013 The International Biometric Society
Deposited On: 16 Dec 2013 00:38
Last Modified: 17 Dec 2013 22:00

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