Short-term randomized controlled clinical trials often provide the basis of regulatory approval for a new drug application, while long-term clinical studies are essential for monitoring the (long-term) effectiveness and safety of a drug. As the duration of a study increases and the number of patients continuing in the study declines, missing data become more of a problem, as they may bias the results. Therefore, standard analytical strategies used in short-term trials (intention-to-treat, per-protocol) may not always be appropriate for data generated in long-term studies. In this article, commonly used analytical approaches in the assessment of clinical trial data will be reviewed. Given their specific characteristics, regulatory authorities and expert guidelines suggest to use several of these approaches in parallel to correctly interpret the data of long-term clinical studies and to come to better informed decisions.