Causal Inference on Win Ratio for Observational Data with Dependent Subjects
存在个体依赖的观察数据中胜比的因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接针对观察研究的混杂调整提出因果胜比估计量，并处理比较组的协变量平衡。
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存在个体依赖的观察数据中胜比的因果推断
- 研究问题
- 在患者嵌套于医院的观察研究中，如何同时处理基线混杂与组内依赖，以胜比衡量具有优先级的复合结局处理效应？
- 方法与思路
- 作者提出使用校准权重的加权分层因果胜比估计量，使比较组在患者层面协变量及聚类效应分布上取得平衡。
- 材料陈述的贡献
- 摘要称模拟中偏差、方差估计、第一类错误和功效表现良好，并将估计量用于儿童创伤性脑损伤的观察研究。
- 阅读关注点
- 应核对因果胜比的目标定义、校准约束与聚类效应的处理，以及处理分配层级、权重可行性和方差估计条件。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明完整混杂控制假设，模拟表现不能保证任意聚类结构下有效。
原文摘要
Composite endpoints are commonly used with an anticipation that clinically relevant endpoints as a whole would yield meaningful treatment benefits. The win ratio is a rank-based statistic to summarize composite endpoints, allowing prioritizing the important components of the composite endpoints. Recent development in statistical inference for the win ratio statistic has been focusing on independent subjects without any potential confounding. When analyzing composite endpoints using observational data, one of the important challenges is confounding at baseline. Additionally, hierarchical observational data structures are commonly seen in practice, especially in multi-center studies with patients nesting within hospitals. Such hierarchical structure can introduce potential dependency or cluster effects among observations in the analysis. To address these two issues when using the win ratio statistic, we propose a weighted stratified causal win ratio estimator with calibrated weights. The calibrated weights create balanced patient-level covariates and cluster effect distributions between comparison groups. We conducted extensive simulation studies and showed promising performance of the proposed estimator in terms of bias, variance estimation, type I error and power analysis, regardless of the allocation of treatment assignments at baseline and intra-cluster correlations within clusters. Lastly, the proposed estimator was applied to an observational study among children with traumatic brain injury.