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Longitudinal Data Analysis for Discrete and Continuous Outcomes
Scott L. Zeger and Kung-Yee Liang
Vol. 42, No. 1 (Mar., 1986), pp. 121-130
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2531248
Page Count: 10
You can always find the topics here!Topics: Correlations, Longitudinal data, Consistent estimators, Health outcomes, Statistical variance, Biometrics, Statistical estimation, Time dependence, Analytical estimating, Children
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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.
Biometrics © 1986 International Biometric Society