JSM 2005 Online Program

Abstract #302480

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	303
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	IMS
Abstract - #302480
Title:	Estimating Equations for Marginal Regression Analysis of Longitudinal Data with Time-Dependent Covariates
Author(s):	Dylan Small*+ and Tze L. Lai
Companies:	The University of Pennsylvania and Stanford University
Address:	464 Huntsman Hall, 3730 Walnut Street, Philadelphia, PA, 19104, USA
Keywords:	estimating equations ; generalized method of moments ; longitudinal data ; time-dependent covariates
Abstract:	Generalized estimating equations (GEE) are widely used for marginal regression analysis of longitudinal data. An appealing feature of the GEE approach is its use of a ``working hypothesis'' about the parts of the model considered nuisance parameters. If the working hypothesis is correct, GEE provides efficient estimates; if the working hypothesis is incorrect, GEE still provides consistent estimates for the parameters of interest. In the presence of time-dependent covariates, it has been noted that only certain types of working hypotheses (in particular, a working hypothesis of independence between all observations) can be used if the researcher wants to maintain consistency in the presence of misspecification of the correlation structure of the observations. This paper presents an approach to marginal regression analysis of longitudinal data that provides more efficient estimates than GEE with working independence when there are time-dependent covariates, but is consistent regardless of the correlation structure of the observations.

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