Extended Generalized Estimating Equations for Clustered Data

Daniel B. Hall*, Thomas A Severini

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Typically, analysis of data consisting of multiple observations on a cluster is complicated by within-cluster correlation. Estimating equations for generalized linear modeling of clustered data have recently received much attention. This article proposes an extension to the generalized estimating equation method proposed by Liang and Zeger, which treats within-cluster correlations as nuisance parameters. Using ideas from extended quasi-likelihood, estimating equations for regression and association parameters are provided simultaneously. The resulting estimators are proven to be asymptotically normal and consistent under certain conditions. The consistency of regression estimators allows incorrect modeling of the correlation among repeated responses. The method is illustrated with an analysis of data from a developmental toxicity study.

Original languageEnglish (US)
Pages (from-to)1365-1375
Number of pages11
JournalJournal of the American Statistical Association
Volume93
Issue number444
DOIs
StatePublished - Dec 1 1998

Keywords

  • Correlation
  • Extended quasi-likelihood
  • Generalized linear model
  • Longitudinal data
  • Marginal model
  • Quasi-likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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