Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

中文翻译：

---

用于整合暴露、基因组和表型数据的因果中介分析


因果中介分析为整合各种类型的暴露、基因组和表型数据提供了一个有吸引力的框架。最近，这个领域的兴趣激增，这主要是由于健康和社会科学中对因果中介分析的需求日益增长。本文旨在回顾中介分析的最新发展，包括单个中介人和大量中介的中介分析，以及具有多重曝光和中介的中介分析。我们的综述侧重于因果中介分析的统计推断的最新进展，特别是在高维中介分析的背景下。我们深入研究了测试中介效应的复杂性，特别是解决了测试大量复合零假设的挑战。通过广泛的仿真研究，我们在一系列场景中比较了现有方法。我们还包括对 Normative Aging Study 数据的分析，该研究检查 DNA 甲基化 CpG 位点作为吸烟状况对肺功能影响的潜在介质。我们讨论了这些方法的优缺点和未来的研究方向。