The current estimand discussion primarily focuses on the analysis of longitudinal data. In this presentation I will discuss how heterogeneously defined time-to-event endpoints are in many indications with respect to what events are ignored, censored, or considered an event for a composite endpoint. The impact of this heterogeneity on effect estimates and statistical significance will be illustrated. Comparing two recent oncology Phase 3 trials I will show that the same intercurrent event, new anti-lymphoma therapy, may play a very different role as an intercurrent event for the primary endpoint progression-free survival and that this difference needs to be appreciated during the design stage already. I will advocate that time-to-event endpoints should also be embedded in the four-component framework brought forward by the ICH E9 working group clearly defining the (1) population of interest, (2) variable, (3) intervention effect of interest, and (4) summary measure. Sharing feedback from regulatory statisticians I will emphasize the difference between rejecting the null hypothesis and quantifying the effect and will comment on the current discussion about study planning and effect quantification for non-proportional hazards in this context.