Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies
匹配观察性研究中隐性偏差分位数的敏感性分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究匹配观察性研究中未测混杂的因果敏感性分析；其中分位数描述隐性偏差，并非结局分位数或分布处理效应。
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匹配观察性研究中隐性偏差分位数的敏感性分析
- 研究问题
- 匹配观察性研究存在未测混杂时，如何以各匹配组隐性偏差的分位数衡量结论敏感性，避免只看最大偏差的保守性？
- 方法与思路
- 作者推广 Rosenbaum 敏感性分析框架，针对匹配组隐性偏差的分位数构造分析，并讨论一般结局、检验统计量和有界零假设。
- 材料陈述的贡献
- 摘要称，该分析对全部偏差分位数同时有效，并可在统计量满足特定性质时处理有界零假设；作者还说明有配套 R 实现。
- 阅读关注点
- 应核对隐性偏差和分位数参数的定义、同时有效性的保证，以及有界零假设所需统计量性质和计算方法适用范围。
- 阅读边界
- 依据 arXiv 预印本摘要，不能确认同行评审或期刊接收；摘要未列出完整条件，敏感性分析本身不能排除未测混杂或证明因果性。
原文摘要
Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis in matched observational studies. It investigates what magnitude the maximum of hidden biases from all matched sets needs to be in order to explain away the observed association. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when investigators suspect that some matched sets may have exceptionally large hidden biases. In this paper, we generalize Rosenbaum's framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, the proposed sensitivity analysis is simultaneously valid across all quantiles of hidden biases and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. In addition, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses when the test statistic satisfies certain properties. An R package implementing the proposed approach is available online.