Analyzing longitudinal data and use of the generalized linear model in health and social sciences

In the health and social sciences, longitudinal data have often been analyzed without taking into account the dependence between observations of the same subject. Furthermore, consideration is rarely given to the fact that longitudinal data may come from a non-normal distribution. In addition to describing the aims and types of longitudinal designs this paper presents three approaches based on generalized estimating equations that do take into account the lack of independence in data, as well as the type of distribution. These approaches are the marginal model (population-average model), the random effects model (subject-specific model), and the transition model (Markov model or auto-correlation model). Finally, these models are applied to empirical data by means of specific procedures included in SAS, namely GENMOD, MIXED, and GLIMMIX.