Shrinkage Empirical Likelihood Estimator in Longitudinal Analysis with Time-Dependent Covariates-Application to Modeling the Health of Filipino Children

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,...

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Bibliographic Details
Published in:Biometrics 2013-09, Vol.69 (3), p.624-632
Main Authors: Leung, Denis Heng-Yan, Small, Dylan S., Qin, Jing, Zhu, Min
Format: Article
Language:English
Subjects:
Adolescent
BIOMETRIC METHODOLOGY
Biometry - methods
Body Mass Index
Child
Child, Preschool
Children & youth
Empirical likelihood
Estimating functions
Estimating techniques
Female
Generalized estimating equations
Health Status
Health Status Indicators
Hispanic Americans
Humans
Infant
Likelihood Functions
Longitudinal data
Longitudinal Studies
Male
Models, Statistical
Philippines
Time & motion studies
Time Factors
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Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12039