Longitudinal data analysis using generalized linear models

Kung Yee Liang, Scott L. Zeger

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the pioposecl estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.

Original languageEnglish (US)
Pages (from-to)13-22
Number of pages10
JournalBiometrika
Volume73
Issue number1
DOIs
StatePublished - Apr 1986

Keywords

  • Estimating equation
  • Generalized linear model
  • Longitudinal data
  • Quasi-likelihood
  • Repeated measures

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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