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Cited by 7,764

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Longitudinal Data Analysis for Discrete and Continuous Outcomes

Zeger, Scott L., Liang, Kung-Yee·1986·Biometrics
DOI 10.2307/2531248
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Abstract

Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.

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Metadata

Authors
Zeger, S. L., & Liang, K.-Y.
Venue
Biometrics
Publisher
JSTOR
Published
1986-03
Volume
42
Issue
1
Pages
121
ISSN
0006-341X (ISSN-L)
DOI
10.2307/2531248
OpenAlex
W1978136073
Cited by
7,764
References
1
FWCI
8.43
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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