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Models for Intensive Longitudinal Data$
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Theodore A. Walls and Joseph L. Schafer

Print publication date: 2006

Print ISBN-13: 9780195173444

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195173444.001.0001

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Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations

Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations

Chapter:
(p.38) 2 Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations
Source:
Models for Intensive Longitudinal Data
Author(s):

Joseph L. Schafer

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195173444.003.0002

The growth of semiparametric regression modeling through generalized estimating questions (GEE) is one of the most influential recent developments in statistical practice. GEE methods are attractive both from a practical and theoretical perspective; they are easy to use, flexible, and make relatively weak assumptions about the distribution of the response of interest. They are closely linked to multilevel models and are commonly regarded as robust relatives of the linear mixed model characterized by Hedeker et al. Because of longstanding tensions existing between two different schools of statistical thought, some who handle longitudinal data may rely either on multilevel models or GEE but not both. The authors see them as complementary instead of referring to the two as rivals.

Keywords:   regression modeling, generalized estimating equations, multilevel models, mixed model, Hedeker

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