For the analysis of a time-to-event endpoint in a single-arm or randomized clinical trial it is generally perceived that interpretation of a given estimate of the survival function, or the comparison between two groups, hinges on some quantification of the amount of follow-up. Typically, a median of some loosely defined quantity is reported. However, whatever median is reported, is typically not answering the question(s) trialists actually have in terms of follow-up quantification. In this paper, inspired by the estimand framework, we formulate a comprehensive list of relevant scientific questions that trialists have when reporting time-to-event data. We illustrate how these questions should be answered, and that reference to an unclearly defined follow-up quantity is not needed at all. In drug development, key decisions are made based on randomized controlled trials, and we therefore also discuss relevant scientific questions not only when looking at a time-to-event endpoint in one group, but also for comparisons. We find that different thinking about some of the relevant scientific questions around follow-up is required depending on whether a proportional hazards assumption can be made or other patterns of survival functions are anticipated, e.g. delayed separation, crossing survival functions, or the potential for cure. We conclude the paper with practical recommendations.

翻译：在单臂或随机临床试验中分析时间-事件终点时，通常认为对生存函数估计值的解读，或两组间的比较，取决于对随访量的某种量化。通常，会报告某个定义模糊的中位数。然而，无论报告何种中位数，通常都无法回答试验者在随访量化方面实际关心的问题。本文受估计目标框架启发，系统梳理了试验者在报告时间-事件数据时需解答的一系列相关科学问题。我们阐释了如何回答这些问题，并指出完全无需引用定义不明确的随访量。在药物开发中，关键决策基于随机对照试验，因此我们不仅讨论单组时间-事件终点分析，还讨论了组间比较中的相关科学问题。研究发现，根据是否可设定比例风险假设，或是否预期其他生存函数模式（如延迟分离、交叉生存函数或潜在治愈可能），对随访相关科学问题的思考方式需相应调整。本文最后提出了实用建议。