随时有效（Anytime-Valid）对数秩检验：无时限连续监测下的误差控制
The Anytime-Valid Logrank Test: Error Control Under Continuous Monitoring with Unlimited Horizon
AI总结：
本研究提出随时有效（AV）对数秩检验，属基于E变量的鞅检验，可在可选停止/继续、无预设样本量的序贯场景下控制I类错误，可扩展至结、Cox回归等，效能与O'Brien-Fleming法相近且期望样本量有竞争力。
摘要：
我们提出了随时有效（anytime-valid, AV）对数秩检验，这是对数秩检验的一种变体，可在可选停止和可选继续的场景下提供I类错误保证。该检验属于序贯检验，无需指定最大样本量或停止规则，还支持带I类错误控制的累积Meta分析，且可扩展定义随时有效置信区间。\n该对数秩检验是近期发展的基于E变量的鞅检验的一个实例。我们在比例风险的半参数设定下证明了该检验的I类错误保证，并展示了如何将其扩展至结、Cox回归和置信序列。通过对对数秩统计量采用高斯近似，我们证明本身始终精确的AV对数秩检验，其拒绝域与O'Brien-Fleming alpha消耗函数法相近，但可通过可选继续实现100%的检验效能。尽管我们的研究设计方法需要更大的样本量，但通过可选停止，其期望样本量具有竞争力。
英文摘要：
We introduce the anytime-valid (AV) logrank test, a version of the logrank test that provides type-I error guarantees under optional stopping and optional continuation. The test is sequential without the need to specify a maximum sample size or stopping rule, and allows for cumulative meta-analysis with type-I error control. The method can be extended to define anytime-valid confidence intervals. The logrank test is an instance of the martingale tests based on E-variables that have been recently developed. We demonstrate type-I error guarantees for the test in a semiparametric setting of proportional hazards and show how to extend it to ties, Cox' regression and confidence sequences. Using a Gaussian approximation on the logrank statistic, we show that the AV logrank test (which itself is always exact) has a similar rejection region to O'Brien-Fleming alpha-spending but with the potential to achieve 100% power by optional continuation. Although our approach to study design requires a larger sample size, the *expected* sample size is competitive by optional stopping.
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。
