AI 中文总结
本文针对带时间-事件终点的协变量调整分析，证明平衡权重法与数据增强法、边缘Cox得分法一阶等价，校准加权无需拟合模型即可达到目标，还通过模拟和REWIND试验验证了结果并给出方差估计建议。
AI 中文摘要
协变量调整可提高随机临床试验中治疗效应分析的效率，前提是调整针对正确的量。对于时间-事件终点，两个边缘目标尤为重要：用于检测治疗效应存在性的对数秩检验，以及用于衡量其大小的边缘风险比。现有协变量调整方法通过不同方式实现这些目标：数据增强通过在每个组内将衍生结局对基线协变量进行回归来调整对数秩得分；而权重法则在形成生存比较前对两组进行重加权以平衡协变量，其中逆概率加权通过拟合的倾向得分模型实现，校准加权则直接求解匹配协变量均值的权重。本文中，我们首先开发了针对时间-事件终点的平衡权重，涵盖校准权重（稳定平衡权重与熵平衡）和倾向得分权重，并证明任何平衡正则化权重均与增强对数秩得分及边缘Cox得分的根具有一阶等价性。因此，这三种方法在一阶层面给出相同的估计量，且校准法无需拟合任何模型即可达到该估计量。加权程序由此继承了数据增强方法的有效性与保证的效率提升。此外，我们表明效率提升随调整协变量的预后强度增大而增长，而实际需注意的问题在于方差估计，对此我们给出建议以防范有限样本下的I类错误膨胀。我们进一步通过模拟研究和REWIND心血管试验的分析验证了上述结果。
英文摘要
Covariate adjustment improves the efficiency of treatment-effect analyses in randomized clinical trials, provided the adjustment targets the correct quantity. For time-to-event endpoints, two marginal targets are of primary interest: the log-rank test for the presence of a treatment effect and the marginal hazard ratio for its magnitude. Existing covariate adjustment approaches reach these targets by different ways. Augmentation adjusts the log-rank score by regressing derived outcomes on the baseline covariates within each arm. Weighting instead reweights the two arms to balance the covariates before the survival comparison is formed: inverse probability weighting does so through a fitted propensity model, while calibration weighting solves directly for weights that match covariate means. In this manuscript, we first develop balancing weighting for time-to-event endpoints, covering both calibration weights (stable balancing weights and entropy balancing) and propensity score weights, and prove that any balancing-regular weighting is first-order equivalent to the augmented log-rank score and to the root of the marginal Cox score. All three routes therefore deliver the same estimator to first order, and calibration reaches it without fitting any model. The weighted procedures thereby inherit the validity and guaranteed efficiency gain of the augmentation approach. In addition, we show that the efficiency gain grows with the prognostic strength of the adjustment covariates, while the practical caveat lies in variance estimation, for which we give recommendations to guard against finite-sample Type I error inflation. We further confirm our results through simulation studies and an analysis of the REWIND cardiovascular trial.
