协变量调整对数秩检验的有限样本校正
Finite-sample correction for the covariate-adjusted log-rank test
- Universitat Politècnica de Catalunya - BarcelonaTech (UPC)（加泰罗尼亚理工大学）
- Novartis Pharma AG（诺华制药股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对协变量调整对数秩检验小样本下I类错误率膨胀问题，提出考虑残差自由度损失与回归系数不确定性的有限样本校正，经模拟验证可有效降低该错误率膨胀。
AI中文摘要：
协变量调整对数秩检验是针对含时间-事件终点的随机试验进行协变量调整的新方法，相比标准对数秩检验可保证效率提升，但该方法在小样本中可能导致I类错误率膨胀，在分配不均衡或调整协变量数量相对样本量较大的试验中尤为明显。本文针对协变量调整对数秩检验统计量的分母提出有限样本校正，该校正考虑了残差自由度损失及未知回归系数的不确定性。模拟结果显示，应用该校正可在多种场景下大幅降低I类错误率膨胀。
英文摘要：
The covariate-adjusted log-rank test is a novel method for covariate adjustment in randomized trials with time-to-event endpoints, offering guaranteed efficiency gains compared to the standard log-rank test. However, it has been noted that, in small samples, this method may lead to type I error rate inflation. This issue is particularly pronounced in trials with imbalanced allocation and settings where the number of adjustment covariates is large relative to the sample size. We propose a finite-sample correction for the denominator of the covariate-adjusted log-rank test statistic that accounts for the loss of the residual degrees of freedom as well as the uncertainty in the unknown regression coefficients. In simulations, we show that applying this correction leads to a substantial reduction in the type I error rate inflation across multiple scenarios.
