Analysis of Longitudinal Data in Stata, Splus and SAS

Longitudinal data are commonly collected in experimental and observational studies, where both disease and risk factors are measured at different repeated times. The goal of this project is to compare analyses performed using Stata, SPlus and SAS under two different families of distributions: normal and logistic. I will show the results obtained from the analyses of two sample data sets; these will analysed using both Generalized Estimating Equation (gee) and Random Effect models. In Stata I will use both the xt programs and the routine provided by Rabe-Hesketh (glamm6): confidence intervals, hypothesis testing and model fitting will be discussed. Missing data issues will be raised and discussed as well.