The primary interest in trials using recurrent event endpoints is usually to understand how treatment affects the occurrence of recurrent events. This raises the question how to measure a treatment effect under the repeated occurrence of an event, which in turn depends critically on the underlying scientific question. Depending on the specific setting, some estimands may be more appropriate than others. In this talk, we discuss the value and limitations of different treatment effect measures (estimands) and their associated statistical analyses (estimators) for recurrent events. First, we consider settings where terminal events such as death are rare, e.g. in relapsing-remitting multiple sclerosis (RRMS) trials. Second, we investigate settings where terminal events are more common, e.g. in chronic heart failure (CHF) trials with death as a terminal event. The estimands are further illustrated by two case studies for RRMS and CHF. In addition, we perform a simulation study to validate the appropriateness of the estimators by comparing their estimates with the targeting estimand value for each estimand under different model assumptions.