Longitudinal data analysis for discrete and continuous outcomes

Biometrics. 1986 Mar;42(1):121-30.


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.

MeSH terms

  • Child
  • Female
  • Humans
  • Longitudinal Studies*
  • Morbidity
  • Mother-Child Relations
  • Regression Analysis
  • Socioeconomic Factors
  • Stress, Psychological