Statistics Papers

Document Type

Journal Article

Date of this Version

2-2007

Publication Source

Journal of the Royal Statistical Society: Series B

Volume

69

Issue

1

Start Page

79

Last Page

99

DOI

10.1111/j.1467-9868.2007.00578.x

Abstract

We develop a new approach to using estimating equations to estimate marginal regression models for longitudinal data with time-dependent covariates. Our approach classifies time-dependent covariates into three types—types I, II and III. The type of covariate determines what estimating equations can be used involving the covariate. We use the generalized method of moments to make optimal use of the estimating equations that are made available by the covariates. Previous literature has suggested the use of generalized estimating equations with the independent working correlation when there are time-dependent covariates. We conduct a simulation study that shows that our approach can provide substantial gains in efficiency over generalized estimating equations with the independent working correlation when a time-dependent covariate is of types I or II, and our approach remains consistent and comparable in efficiency with generalized estimating equations with the independent working correlation when a time-dependent covariate is of type III. We apply our approach to analyse the relationship between the body mass index and future morbidity among children in the Philippines.

Copyright/Permission Statement

This is the peer reviewed version of the following article: Lai, T. L. and Small, D. (2007), Marginal regression analysis of longitudinal data with time-dependent covariates: a generalized method-of-moments approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69: 79–99., which has been published in final form at doi: 10.1111/j.1467-9868.2007.00578.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms.

Keywords

estimating equations, generalized method of moments, longitudinal data analysis, marginal regression, time-dependent covariates, working hypothesis

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Date Posted: 27 November 2017

This document has been peer reviewed.