There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
<p class="first" id="d9128088e44">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.
</p>