Advances in analysis of longitudinal data

Abstract

In this review, we explore recent developments in the area of linear and nonlinear generalized mixed-effects regression models and various alternatives, including generalized estimating equations for analysis of longitudinal data. Methods are described for continuous and normally distributed as well as categorical (binary, ordinal, nominal) and count (Poisson) variables. Extensions of the model to three and four levels of clustering, multivariate outcomes, and incorporation of design weights are also described. Linear and nonlinear models are illustrated using an example involving a study of the relationship between mood and smoking.

Figures

Figure 1

Random intercept model.

Figure 2

Random intercept and trend model.

Figure 3

Logistic relationship between x and the response probability.

Figure 4

Linear relationship between x and the logit.