Cross-Screening in Observational Studies That Test Many Hypotheses
多假设观察性研究中的交叉筛选AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确针对观察性研究中的多重因果假设、非随机分配偏差与敏感性分析，直接对应因果推断方向。
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多假设观察性研究中的交叉筛选
- 研究问题
- 观察性研究同时检验大量因果假设时，如何使结论兼顾多重检验控制与对非随机处理分配造成的未观测偏差的稳健性？
- 方法与思路
- 作者随机将样本分半，用各半样本为另一半规划分析，再对两项研究采用 Bonferroni 校正，报告其中更有利的结论。
- 材料陈述的贡献
- 摘要称，交叉筛选无需两半结论一致即可强控制族错误率，并在中等样本的大量假设敏感性分析中具有较高功效。
- 阅读关注点
- 应核对两半样本可用于规划的内容、敏感性分析的偏差模型，以及族错误率证明、复制性定义和相对功效的适用场景。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明，不能推断刊本变化；具体理论条件和功效幅度未说明，摘要也指出少量假设的随机研究中吸引力有限。
原文摘要
We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for multiple testing: In the absence of bias, it is extremely improbable that multiple testing alone would create an association insensitive to moderate biases. We propose a new strategy called “cross-screening,” different from but motivated by recent work of Bogomolov and Heller on replicability. Cross-screening splits the data in half at random, uses the first half to plan a study carried out on the second half, then uses the second half to plan a study carried out on the first half, and reports the more favorable conclusions of the two studies correcting using the Bonferroni inequality for having done two studies. If the two studies happen to concur, then they achieve Bogomolov–Heller replicability; however, importantly, replicability is not required for strong control of the family-wise error rate, and either study alone suffices for firm conclusions. In randomized studies with just a few null hypotheses, cross-screening is not an attractive method when compared with conventional methods of multiplicity control. However, cross-screening has substantially higher power when hundreds or thousands of hypotheses are subjected to sensitivity analyses in an observational study of moderate size. We illustrate the technique by comparing 46 biomarkers in individuals who consume large quantities of fish versus little or no fish. The R package CrossScreening on CRAN implements the cross-screening method. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.