The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by Hernán (Epidemiology (Cambridge, Mass) 21(1):13–15, 2010) in a commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individuals who survived a given period of time, populations that will typically fail to be comparable—even in a randomised trial—as a result of different pressures or intensities acting on different populations. The commentary has been very influential, but also a source of surprise and confusion. In this note, we aim to provide more insight into the subtle interpretation of hazard ratios and differences, by investigating in particular what can be learned about a treatment effect from the hazard ratio becoming 1 (or the hazard difference 0) after a certain period of time. We further define a hazard ratio that has a causal interpretation and study its relationship to the Cox hazard ratio, and we also define a causal hazard difference. These quantities are of theoretical interest only, however, since they rely on assumptions that cannot be empirically evaluated. Throughout, we will focus on the analysis of randomised experiments.

中文翻译：

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危险对比解释的微妙之处。

风险比是随机试验中最常报告的治疗效果指标之一，但也是很多误解的根源。Hernán (Epidemiology (Cambridge, Mass) 21(1):13–15, 2010) 在一篇评论中阐明了这一点，该评论强调风险比对比了在给定时间段内幸存下来的接受治疗和未接受治疗的人群，由于作用于不同人群的压力或强度不同，通常无法比较的人群——即使在随机试验中也是如此。评论非常有影响力，但也令人惊讶和困惑。在本说明中，我们旨在更深入地了解风险比和差异的微妙解释，通过特别调查可以从一定时间段后风险比变为 1（或风险差为 0）中了解治疗效果的信息。我们进一步定义了一个具有因果解释的风险比，并研究了它与 Cox 风险比的关系，我们还定义了一个因果风险差异。然而，这些数量仅具有理论意义，因为它们依赖于无法通过经验评估的假设。在整个过程中，我们将专注于随机实验的分析。因为它们依赖于无法通过经验评估的假设。在整个过程中，我们将专注于随机实验的分析。因为它们依赖于无法通过经验评估的假设。在整个过程中，我们将专注于随机实验的分析。