In a follow-up study, patients are monitored over time. Longitudinal and time-to-event studies are the two most important types of a follow-up study. In this paper, the focus is on longitudinal studies with a continuous response where patients are examined at several time points. While longitudinal studies provide a powerful tool for the evaluation of a treatment effect over time, a major problem is missing data caused, for example, by patients who drop out from the study. Many longitudinal studies in rheumatology use inappropriate statistical methodology because either they do not address correctly the correlated nature of the repeated measurements, or they treat the problem of missing data incorrectly. We will illustrate that there are interpretational and computational issues with the 'classical' approaches. Further, we expand here on more appropriate statistical techniques to analyze longitudinal studies. To this end, we focus on randomized controlled trials (RCTs) and illustrate the approaches on data from a fictive randomized controlled trial in rheumatology.