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.
Keywords
CovariateGeneralized estimating equationGeeEstimating equationsMathematicsOutcome (game theory)StatisticsMarginal modelLongitudinal dataEconometricsRegression analysisVariance (accounting)Computer scienceMaximum likelihoodData miningMathematical economics
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Publication Info
- Year
- 1986
- Type
- article
- Volume
- 42
- Issue
- 1
- Pages
- 121-121
- Citations
- 7742
- Access
- Closed
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Cite This
Scott L. Zeger,
Kung‐Yee Liang
(1986).
Longitudinal Data Analysis for Discrete and Continuous Outcomes.
Biometrics
, 42
(1)
, 121-121.
https://doi.org/10.2307/2531248
Identifiers
- DOI
- 10.2307/2531248