AI 中文总结
研究大规模中介假设检验问题，提出两阶段自适应检验框架，第一阶段筛选减少待检假设数，第二阶段采用多种控制方法，能渐近控制错误发现率，在多场景下功效良好。
AI 中文摘要
在检验大规模中介假设时，从同一样本中为每个假设获得的暴露-中介和中介-结果路径特定p值可合并为一对渐近独立的p值，分别用于检验全局原假设和复合中介原假设。针对更严格全局原假设的第一阶段筛选程序可有效减少第二阶段要检验的中介假设数量，降低多重比较的保守性。该框架可纳入第一阶段筛选阈值的任何数据自适应选择及第二阶段的任何逐步错误发现率控制方法。所提程序渐近控制错误发现率，在广泛场景中始终具有良好功效。
英文摘要
In testing large-scale mediation hypotheses, the exposure-mediator and mediator-outcome path-specific p-values obtained for each hypothesis from the same sample can be combined into a pair of asymptotically independent p-values, which can then be used to test a global null hypothesis and a composite mediation null hypothesis, respectively. A first-stage screening procedure targeting the more stringent global null can effectively reduce the number of mediation hypotheses to be tested in the second stage, which in turn reduces the conservativeness of multiple comparison. The framework can incorporate any data-adaptive choice of screening threshold in Stage 1, and any step-up false discovery rate control method in Stage 2. The proposed procedure controls the false discovery rate asymptotically while being consistently well powered across a wide range of scenarios.
Comments28 pages, 7 figures. Updated simulation implementations to use the authors' released M-DACT code. Results were updated accordingly and the main conclusions are unchanged. Added code and data availability information. Code: https://github.com/keyyyyyx/Two-stage-Adaptive-Mediation-Testing
