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Suppose someone enters observation six months after the clinical event. With that event as the origin, a one-year horizon falls six months after entry, and their risk-set contribution begins at month six. Resetting the clock at entry would make one year refer to month 18 after the clinical event. That changes which period the survival estimate describes, so the two estimates would answer different timing questions.

Suppose anomaly labels require later follow-up measurements, so evaluating them as predictions at resection would credit information unavailable at that prediction time.

Two interpretations conflict only after their population, treatment conditions, outcome, time horizon, summary measure, and intercurrent-event strategy match. In a time-to-event analysis, that includes checking whether both report the same quantity, since a hazard ratio and a fixed-horizon risk difference can support different decisions even when derived from the same trial. Only then does comparing their effect estimates test a genuine disagreement. Otherwise, the practical task is to map each estimand to its decision, such as expected benefit under routine discontinuation patterns versus efficacy under continued treatment.

The choice changes what “high risk” can be used to decide. If the aim is counseling or scheduling surveillance over a fixed horizon, cumulative incidence is the more direct quantity because it estimates the observed probability of the outcome while retaining competing deaths in the probability structure. A cause-specific hazard instead compares the event rate among people currently alive and event-free, which can help study associations but does not by itself give the probability of experiencing the event by that horizon. Treating death as ordinary censoring can therefore preserve a ranking model yet miscalibrate absolute-risk thresholds used to trigger closer follow-up. Additional treatment and loss of eligibility need separate handling: they are competing events only if they preclude the endpoint as defined; otherwise they may be intercurrent events or observation loss. The missing methods still prevent deciding which interpretation PMID 42407332 supports, but the downstream check is concrete: identify whether the anomaly label is meant to rank instantaneous risk or guide an absolute-risk decision over time.