Longitudinal data analysis using generalized linear models (original) (raw)

Journal Article

,

Department of Biostatistics, Johns Hopkins University

Baltimore, Maryland 21205, U.S.A.

Search for other works by this author on:

Department of Biostatistics, Johns Hopkins University

Baltimore, Maryland 21205, U.S.A.

Search for other works by this author on:

Received:

01 January 1986

Revision received:

01 October 1986

• PDF
• Split View
• • Article contents
  • Figures & tables
  • Video
  • Audio
  • Supplementary Data

Navbar Search Filter Mobile Enter search term Search

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.

This content is only available as a PDF.

/ 1986 Biometrika Trust

Citations

Views

Altmetric

Metrics

Total Views 38,869

6,032 Pageviews

32,837 PDF Downloads

Since 11/1/2016

×

Email alerts

Citing articles via

• Latest

• Most Read

• Most Cited

More from Oxford Academic