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 …

This article discusses extensions of generalized linear models for the analysis of
longitudinal data. Two approaches are considered: subject-specific (SS) models in which …

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 analysis of data from longitudinal studies requires special techniques, which take into
account the fact that the repeated measurements within one individual are correlated. In this …

We provide sufficient conditions for estimating from longitudinal data the causal effect of a
time-dependent exposure or treatment on the marginal probability of response for a …

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 …

For analyses of longitudinal repeated‐measures data, statistical methods include the
random effects model, fixed effects model and the method of generalized estimating …

This paper considers extensions of logistic regression to the case where the binary outcome
variable is observed repeatedly for each subject. We propose two working models that lead …

This paper reviews statistical methods for the analysis of discrete and continuous
longitudinal data. The relative merits of longitudinal and cross‐sectional studies are …

The method of generalized estimating equations (GEE) is often used to analyze longitudinal
and other correlated response data, particularly if responses are binary. However, few …