Beyersmann Jan, Schmoor Claudia, Schumacher Martin
Institute of Statistics, Ulm University, Ulm, Germany.
Clinical Trials Unit, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Biom J. 2025 Jun;67(3):e70057. doi: 10.1002/bimj.70057.
Censoring makes time-to-event data special and requires customized statistical techniques. Survival and event history analysis therefore builds on hazards as the identifiable quantities in the presence of rather general censoring schemes. The reason is that hazards are conditional quantities, given previous survival, which enables estimation based on the current risk set-those still alive and under observation. But it is precisely their conditional nature that has made hazards subject of critique from a causal perspective: A beneficial treatment will help patients survive longer than had they remained untreated. Hence, in a randomized trial, randomization is broken in later risk sets, which, however, are the basis for statistical inference. We survey this dilemma-after all, mapping analyses of hazards onto probabilities in randomized trials is viewed as still having a causal interpretation-and argue that a causal interpretation is possible taking a functional point of view. We illustrate matters with examples from benefit-risk assessment: Prolonged survival may lead to more adverse events, but this need not imply a worse safety profile of the novel treatment. These examples illustrate that the situation at hand is conveniently parameterized using hazards, that the need to use survival techniques is not always fully appreciated and that censoring not necessarily leads to the question of "what, if no censoring?" The discussion should concentrate on how to correctly interpret causal hazard contrasts and analyses of hazards should routinely be translated onto probabilities.
删失使事件发生时间数据具有特殊性，需要采用定制的统计技术。因此，生存分析和事件史分析基于风险构建，因为在相当一般的删失方案下，风险是可识别的量。原因在于，风险是给定先前生存情况的条件量，这使得能够基于当前风险集（仍存活且处于观察中的个体）进行估计。但正是它们的条件性质使得风险从因果角度受到批评：一种有益的治疗方法会帮助患者比未接受治疗存活更长时间。因此，在随机试验中，随机化在后续风险集中被打破，然而，这些风险集是统计推断的基础。我们审视了这一困境——毕竟，在随机试验中将风险分析映射到概率上仍被视为具有因果解释——并认为从功能角度来看，因果解释是可能的。我们用效益 - 风险评估的例子来说明这些问题：延长的生存期可能会导致更多不良事件，但这不一定意味着新治疗方法的安全性更差。这些例子表明，使用风险可以方便地对当前情况进行参数化，使用生存技术的必要性并不总是被充分理解，而且删失不一定会引发“如果没有删失会怎样？”这样的问题。讨论应集中在如何正确解释因果风险对比，并且风险分析应常规地转化为概率分析。
