Bridging Balancing Weights and Augmentation in Covariate-adjusted Analyses with Time-to-Event Endpoints: Theory and Practical Recommendations
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生存结局协变量调整中平衡加权与增广方法的联系
- 研究问题
- 在随机临床试验的生存结局分析中，平衡加权与增广调整能否针对相同的边际检验及效应目标，并获得一致的效率收益？
- 方法与思路
- 作者发展校准权重和倾向评分权重，分析其与增广 log-rank 得分及边际 Cox 得分根的一阶等价性，并讨论方差估计。
- 材料陈述的贡献
- 摘要称满足平衡正则条件的加权程序与增广方法一阶等价，并继承其有效性和效率收益，同时提出防范有限样本一类错误膨胀的建议。
- 阅读关注点
- 应核对平衡正则条件、边际风险比的具体目标、删失处理方式，以及方差估计建议如何适配不同权重和有限样本情形。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列明等价性与效率保证的完整条件，一阶等价也不保证有限样本结果相同。
原文摘要
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.