Sensitivity analysis for observational studies with flexible matched designs
灵活匹配设计下观察性研究的敏感性分析AI 解读 ↓
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灵活匹配设计下观察性研究的敏感性分析
- 研究问题
- 实际匹配难以达到精确匹配时，如何检验因果结论对未观测混杂的稳健性，并为既有敏感性分析程序提供有效依据？
- 方法与思路
- 作者保留Rosenbaum方法的分析程序，但将随机性依据改为潜在结果的随机置换，而非处理分配的随机化。
- 材料陈述的贡献
- 摘要称，新推断框架允许灵活匹配算法，并通过替代随机性来源保障统计有效性，缓解原框架对精确匹配的要求。
- 阅读关注点
- 应核对潜在结果置换成立的具体条件、允许的匹配规则，以及敏感性参数的含义和统计有效性的证明范围。
- 阅读边界
- 仅依据所给Crossref摘要；替代框架的具体假设未说明，允许灵活匹配不等于任意匹配或任意未观测混杂下均有效。
原文摘要
Abstract Observational studies provide invaluable opportunities to draw causal inference, but they may suffer from biases due to pretreatment differences between treated and control units. Matching is a popular approach to reduce observed covariate imbalance. To tackle unmeasured confounding, a sensitivity analysis is often conducted to investigate how robust a causal conclusion is to the strength of unmeasured confounding. For matched observational studies, Rosenbaum proposed a sensitivity analysis framework that uses the randomization of treatment assignments as the ‘reasoned basis’ and imposes no model assumptions on the potential outcomes, as well as their dependence on the observed and unobserved confounding factors. However, this otherwise appealing framework requires exact matching to guarantee its validity, which is hard to achieve in practice. In this paper we provide an alternative inferential framework that shares the same procedure as Rosenbaum’s approach, but relies on a different justification. Our framework allows flexible matching algorithms and utilizes an alternative source of randomness, in particular random permutations of potential outcomes instead of treatment assignments, to guarantee statistical validity.