• DOI:10.2307/2531248
• Corpus ID: 6986358

Longitudinal data analysis for discrete and continuous outcomes.

@article{Zeger1986LongitudinalDA,
  title={Longitudinal data analysis for discrete and continuous outcomes.},
  author={Scott L. Zeger and Kung Yee Liang},
  journal={Biometrics},
  year={1986},
  volume={42 1},
  pages={
          121-30
        }
}

• S. Zeger, K. Liang
• Published 1 March 1986
• Mathematics
• Biometrics

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.