Missing data frequently occur in clinical trials. An important source for missing data are patients who leave the study prematurely, so-called dropouts. We will concentrate here on the impact of dropouts on the clinical trial analysis. When patients are evaluated under treatment only once, then the presence of dropouts makes it hard to comply with the intention-to-treat (ITT) principle. However, when repeated measurements are taken, then one can make use of the observed portion of the data to retrieve information on dropouts. Generally, commonly used methods to analyze incomplete longitudinal clinical trial data include complete case (CC) analysis and a last observation carried forward (LOCF) analysis. However, these methods rest on strong and unverifiable assumptions about the dropout mechanism. Over the last decades, several longitudinal data analysis methods have been suggested, providing a valid estimate for, e.g., the treatment effect under less restrictive assumptions. The assumptions regarding the dropout mechanism have been classified by Rubin and co-workers as missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We will argue that repeated measures analysis provide valid estimates of the treatment effect under MAR. Finally, as it is impossible to verify that the dropout mechanism is MAR, we argue that, to evaluate the robustness of the conclusion, a sensitivity analysis thereby varying the assumption on the dropout mechanism should become a standard procedure when analyzing the results of a clinical trial.