He Yinqiu, Song Peter X K, Xu Gongjun
Department of Statistics, University of Wisconsin, Madison, WI, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
J R Stat Soc Series B Stat Methodol. 2023 Nov 14;86(2):411-434. doi: 10.1093/jrsssb/qkad129. eCollection 2024 Apr.
Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific fields. Testing for the mediation effect (ME) is greatly challenged by the fact that the underlying null hypothesis (i.e. the absence of MEs) is composite. Most existing mediation tests are overly conservative and thus underpowered. To overcome this significant methodological hurdle, we develop an adaptive bootstrap testing framework that can accommodate different types of composite null hypotheses in the mediation pathway analysis. Applied to the product of coefficients test and the joint significance test, our adaptive testing procedures provide type I error control under the composite null, resulting in much improved statistical power compared to existing tests. Both theoretical properties and numerical examples of the proposed methodology are discussed.
中介分析旨在评估某种暴露是否以及如何通过中间变量影响感兴趣的结果。由于科学领域对这类分析的巨大需求，这个问题最近受到了极大关注。检验中介效应（ME）面临着巨大挑战，因为潜在的原假设（即不存在中介效应）是复合的。大多数现有的中介检验过于保守，因此功效不足。为了克服这一重大方法障碍，我们开发了一种自适应自助检验框架，该框架可以适应中介路径分析中不同类型的复合原假设。应用于系数乘积检验和联合显著性检验，我们的自适应检验程序在复合原假设下提供了第一类错误控制，与现有检验相比，统计功效有了显著提高。文中讨论了所提出方法的理论性质和数值示例。
