Clinical research is often interested in longitudinal follow-up over several visits. All scheduled visits are not carried out and it is not unusual to have a different number of visits by patient. The Generalized Estimating Equations can handle continuous or discrete autocorrelated response. The method allows a different number of visits by patients. The GEE are robust to missing completely at random data, but when the last visits are fewer, the estimator may be biased. We propose a simulation study to investigate the impact of missing visits on the estimators of the model parameters under different missing data patterns. Different types of responses are studied with an exchangeable or autoregressive of order one structure. The number of subjects affected by the missing data and the number of visits removed, vary in order to assess the impact of the missing data. Our simulations show that the esti-mators obtained by GEE are resistant to a certain rate of missing data. The results are homogeneous regardless to the imposed missing data structure.