O'Quigley John
Department of Statistical Science, University College London, London, United Kingdom.
J Clin Oncol. 2022 Oct 20;40(30):3537-3545. doi: 10.1200/JCO.21.01811. Epub 2022 Jun 29.
A statistical test for the presence of treatment effects on survival will be based on a null hypothesis (absence of effects) and an alternative (presence of effects). The null is very simply expressed. The most common alternative, also simply expressed, is that of proportional hazards. For this situation, not only do we have a very powerful test in the log-rank test but also the outcome is readily interpreted. However, many modern treatments fall outside this relatively straightforward paradigm and, as such, have attracted attention from statisticians eager to do their best to avoid losing power as well as to maintain interpretability when the alternative hypothesis is less simple. Examples include trials where the treatment effect decays with time, immunotherapy trials where treatment effects may be slow to manifest themselves as well as the so-called crossing hazards problem. We review some of the solutions that have been proposed to deal with these issues. We pay particular attention to the integrated log-rank test and how it can be combined with the log-rank test itself to obtain powerful tests for these more complex situations.
一项关于治疗对生存是否有影响的统计检验将基于原假设（无影响）和备择假设（有影响）。原假设表述非常简单。最常见的备择假设，表述也很简单，是比例风险假设。对于这种情况，我们不仅在对数秩检验中有一个非常强大的检验，而且结果也很容易解释。然而，许多现代治疗方法并不属于这种相对简单的模式，因此，引起了统计学家的关注，他们急于在备择假设不那么简单的情况下，尽力避免检验效能的损失并保持可解释性。例子包括治疗效果随时间衰减的试验、治疗效果可能表现缓慢的免疫治疗试验以及所谓的交叉风险问题。我们回顾一些为处理这些问题而提出的解决方案。我们特别关注整合对数秩检验以及它如何与对数秩检验本身相结合，以针对这些更复杂的情况获得强大的检验。
