In modern oncology trials, the interpretation of estimated treatment effects depends critically on how the estimands are specified—for example, how events are defined and how intercurrent events are handled, as framed by ICH E9(R1). Although such specifications may appear to be matters of detail, these choices can substantially change what estimated treatment effects represent, and therefore how the resulting evidence should be understood. Drawing on the presenter's experience in the statistical review of oncology trials at the Pharmaceuticals and Medical Devices Agency (PMDA), this presentation examines these challenges through two concrete case examples. The first illustrates the impact of informative censoring, showing how the handling of certain intercurrent events can affect the estimated treatment effects. The second illustrates how the definition of events can shape the estimated treatment effects, potentially yielding results that require careful interpretation. Through these examples, the presentation aims to foster shared understanding between clinicians and statisticians, thereby supporting more reliable evidence generation in cancer clinical trials.