Center for Applied Mathematics, Tianjin University, Tianjin 300072, China.
Department of Population, Family and Reproductive Health, Center On the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Bioinformatics. 2024 Feb 1;40(2). doi: 10.1093/bioinformatics/btae055.
There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum.
The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method's efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight.
Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA.
最近人们对开发高维中介分析方法产生了浓厚的兴趣。然而，大多数中介统计方法严重依赖于均值回归，这限制了它们充分捕捉整个结果分布中复杂中介效应的能力。为了弥补这一差距，我们提出了一种新的方法，用于在整个结果分布范围内选择和测试中介变量。
所提出的高维分位数中介模型提供了一个全面的视角，了解潜在的中介变量如何通过其中介途径影响结果。该方法通过广泛的模拟进行了验证。该研究通过一个实际数据应用案例，检验了 DNA 甲基化对母亲吸烟与子女出生体重之间关系的中介效应。
我们的方法提供了一个公开可用且用户友好的函数 qHIMA()，可以通过 R 包 HIMA 在 https://CRAN.R-project.org/package=HIMA 上访问。
