Covariate-adjusted win statistics in randomized clinical trials with ordinal outcomes
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随机临床试验中有序结果胜出统计量的协变量调整
- 研究问题
- 对于随机临床试验中的有序结果，如何通过基线协变量调整，提高基于两两结果比较的胜出比和胜出差估计精度？
- 方法与思路
- 作者构造逆概率与重叠加权估计量，并用有序结果回归进行增广；借助 U 统计量理论推导渐近分布和闭式方差估计。
- 材料陈述的贡献
- 摘要称，所提协变量调整估计量在工作模型误设下保持一致性；模拟通常显示效率改善，并提供试验应用及 winPSW 实现。
- 阅读关注点
- 应核对两两比较所定义的目标量、随机化如何支撑模型稳健性，以及增广估计的效率例外和方差公式的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明极端例外的具体情形，模拟效率改善不能推广到任意设计。
原文摘要
Ordinal outcomes are common in clinical settings where they often represent increasing levels of disease progression or different levels of functional impairment. In this article, we focus on representing the average treatment effect for ordinal outcomes via intrinsic pairwise outcome comparisons captured through win estimands, such as the win ratio and win difference. Recognizing the value of baseline covariate adjustment toward enhanced precision, we first develop propensity score weighting estimators, including both inverse probability weighting (IPW) and overlap weighting (OW), tailored to estimating win estimands. Furthermore, we develop augmented weighting estimators that leverage an additional ordinal outcome regression to potentially improve efficiency over weighting alone. Leveraging the theory of U-statistics, we establish the asymptotic theory for all estimators, and derive closed-form variance estimators to support statistical inference. We also prove that all of the covariate-adjusted estimators do not compromise consistency for the target estimand even when the associated working models are incorrectly specified; hence these covariate-adjusted estimators are model-robust. Through simulations we demonstrate the enhanced efficiency of the weighted estimators over the unadjusted estimator, with the augmented weighting estimators showing a further improvement in efficiency except for extreme cases. Finally, we illustrate our proposed methods with the ORCHID trial, and implement our covariate adjustment methods in an R package winPSW.