在预期延迟效应下生存分析中假设检验的可视化
Visualizing hypothesis tests in survival analysis under anticipated delayed effects
AI总结：
本文针对存在延迟效应导致非比例风险的时间至事件临床试验，提出一种图形化方法直接比较加权log-rank检验与基于RMST的检验，从而在功效和I类错误之外为选择主要分析方法提供更充分的依据。
摘要：
当我们因延迟效应而预期非比例风险时，对于以时间至事件为终点的随机临床试验（RCT）的主要分析，什么可被视为适当的统计方法？这一问题近来引发了大量讨论。标准方法是log-rank检验和/或Cox比例风险模型。统计文献中已探索了替代方法，例如加权log-rank检验以及基于限制平均生存时间（RMST）的检验。虽然与标准log-rank检验相比，加权log-rank检验可以达到较高的功效，但在特定条件下，某些权重的选择可能导致I类错误膨胀。此外，它们并未与一个明确的估计目标相关联。因此，可以说它们难以解释。另一方面，基于RMST的检验统计量允许研究者考察两条生存曲线在预先指定的时间点$τ$之前的平均差异——这是一个明确的估计目标。然而，由于强调$τ$之前的差异，此类检验统计量可能无法充分捕捉新治疗在长期生存方面的获益。在本文中，我们引入了一种图形化方法，用于直接比较加权log-rank检验与基于RMST的检验。这一新视角使得分析方法的选择更加有据可依，超越了功效和I类错误的比较。
英文摘要：
What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to an unambiguous estimand. Arguably, therefore, they are difficult to intepret. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point $τ$ -- an unambiguous estimand. However, by emphasizing differences prior to $τ$, such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
