AI 中文总结
本研究针对Cox模型，开发了评估亚组处理效应一致性的新检验方法，其功效优于传统TOST检验，并将其应用于CANTOS试验相关案例研究。
AI 中文摘要
临床试验的总体处理效应可能无法充分代表特定患者亚组，导致人群水平的疗效结论能否合法地推广到这些亚组存在不确定性。传统的交互检验仅研究亚组特异性处理效应是否完全相等，无法检测差异是否小到临床上可忽略的程度。我们开发了一个正式框架，用于评估Cox比例风险模型中时间-事件结局的亚组处理效应一致性。一致性被表述为基于（加权）处理-亚组交互系数的等价性问题。我们考虑检测两个互补亚组之间的一致性，以及亚组特异性处理效应与总体处理效应的一致性。对于每种情形，我们开发了传统的双单侧检验程序（TOST），以及一种受正态分布参数最优等价检验启发的新检验。我们证明了所有程序的渐近有效性，并通过实证表明，新检验比对应的TOST检验更具功效。最后，我们将新方法应用于一项由CANTOS心血管结局试验驱动的案例研究。
英文摘要
An overall treatment effect in a clinical trial may inadequately represent particular patient subgroups, creating uncertainty about whether a population-level efficacy conclusion can legitimately be transferred to them. Conventional interaction tests only investigate whether subgroup-specific treatment effects are exactly equal or not, but cannot detect whether the differences are small enough to be clinically negligible. We develop a formal framework for assessing subgroup treatment effect consistency for time-to-event outcomes within the Cox proportional hazards model. Consistency is formulated as an equivalence problem based on the (weighted) treatment-by-subgroup interaction coefficient. We consider detecting consistency between two complementary subgroups and consistency of subgroup-specific treatment effects with the overall treatment effect. For each setting, we develop a conventional two one-sided tests procedure (TOST) and a new test motivated by optimal equivalence testing for normally distributed parameters. We prove the asymptotic validity of all procedures and show empirically that the new tests are more powerful than their TOST counterparts. Finally, we apply the new methodology to a case study motivated by the CANTOS cardiovascular outcomes trial.
