Longitudinal data includes repeated measurements associated with one or more variables for different subjects over time. These data are used in various sciences such as econometrics, social sciences, medicine and agriculture. In order to analyze this type of data, it is necessary to consider two important types of dependency: the intra-class correlation and the serial correlation. The intra-class correlation is due to the effect of the subject characteristics on the observations of the same subject and the serial correlation is due to the dependence of observations to their previous observations over time. To analyze longitudinal data, various models such as mixed-effects, auto-regressive and transition models are used. In mixed-effects models, by considering random effects in the structure of the model, the intra-class correlation is controlled.
