A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses

Hui Zhang*, Q. Yu, C. Feng, D. Gunzler, P. Wu, X. M. Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Poisson log-linear regression is a popular model for count responses. We examine two popular extensions of this model - the generalized estimating equations (GEE) and the generalized linear mixed-effects model (GLMM) - to longitudinal data analysis and complement the existing literature on characterizing the relationship between the two dueling paradigms in this setting. Unlike linear regression, the GEE and the GLMM carry significant conceptual and practical implications when applied to modeling count data. Our findings shed additional light on the differences between the two classes of models when used for count data. Our considerations are demonstrated by both real study and simulated data.

Original languageEnglish (US)
Pages (from-to)2067-2079
Number of pages13
JournalJournal of Applied Statistics
Volume39
Issue number9
DOIs
StatePublished - Sep 1 2012

Keywords

  • generalized estimating equations
  • generalized linear mixed-effect model
  • hotelling's T statistic
  • likelihood ratio test
  • score test

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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