Liang and Zeger proposed an extension of generalized linear models to the analysis of
longitudinal data. Their approach is closely related to quasi‐likelihood methods and can …

We consider a longitudinal study of interstitial cystitis (IC) in women, in which the time
between bouts of repeated measurements is large relative to the within‐bout separation in …

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 …

Longitudinal data occurs frequently in medical studies, particularly in clinical trials. Often, the
response variable is nonnormal, for example, it may be a binary variable, that is,“improved” …

This paper describes an EM algorithm for nonparametric maximum likelihood (ML)
estimation in generalized linear models with variance component structure. The algorithm …

This paper proposes an extension of generalized linear models to the analysis of
longitudinal data. We introduce a class of estimating equations that give consistent …

The study of longitudinal data plays a significant role in medicine, epidemiology and social
sciences. Typically, the interest is in the dependence of an outcome variable on the …

Generalized estimation equations for longitudinal data analysis Page 58 CHAPTER 3
Generalized estimating equations for longitudinal data analysis Stuart Lipsitz and Garrett …

We consider herein diagnostic methods for the quasi-likelihood regression models
developed by Zeger and Liang [Zeger, SL, Liang, K.-Y., 1986, Longitudinal data analysis for …

In this chapter we discuss the generalized estimating equations (GEE) approach for
analyzing longitudinal data. Over the past 20 years, the GEE approach has proven to be an …