BACKGROUND Mediation analysis is used in epidemiology to identify pathways through which exposures influence health. The advent of high-throughput (omics) technologies gives opportunities to perform mediation analysis with a high-dimension pool of covariates. OBJECTIVE We aimed to highlight some biostatistical issues of this expanding field of high-dimension mediation. DISCUSSION The mediation techniques used for a single mediator cannot be generalized in a straightforward manner to high-dimension mediation. Causal knowledge on the relation between covariates is required for mediation analysis, and it is expected to be more limited as dimension and system complexity increase. The methods developed in high dimension can be distinguished according to whether mediators are considered separately or as a whole. Methods considering each potential mediator separately do not allow efficient identification of the indirect effects when mutual influences exist among the mediators, which is expected for many biological (e.g., epigenetic) parameters. In this context, methods considering all potential mediators simultaneously, based, for example, on data reduction techniques, are more adapted to the causal inference framework. Their cost is a possible lack of ability to single out the causal mediators. Moreover, the ability of the mediators to predict the outcome can be overestimated, in particular because many machine-learning algorithms are optimized to increase predictive ability rather than their aptitude to make causal inference. Given the lack of overarching validated framework and the generally complex causal structure of high-dimension data, analysis of high-dimension mediation currently requires great caution and effort to incorporate a priori biological knowledge. https://doi.org/10.1289/EHP6240.

中文翻译：

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高维环境中的调解分析带来的挑战。

背景技术在流行病学中使用中介分析来确定暴露影响健康的途径。高通量（组学）技术的出现提供了使用协变量的高维池进行中介分析的机会。目的我们旨在突出这一高维调解领域不断扩大的一些生物统计学问题。讨论不能将用于单个调解员的调解技术直接简单地推广到高维调解。中介分析需要有关协变量之间关系的因果知识，并且随着维度和系统复杂性的增加，期望因果关系知识将受到更多的限制。可以根据是单独考虑还是整体考虑调解人来区分高维度开发的方法。当介体之间存在相互影响时，分别考虑每个潜在介体的方法不能有效地识别间接效应，这对于许多生物学（例如表观遗传学）参数都是期望的。在这种情况下，例如基于数据缩减技术，同时考虑所有潜在中介者的方法更适合因果推理框架。他们的代价可能是无法找出因果中介者的能力。此外，调解员预测结果的能力可能会被高估，特别是因为许多机器学习算法都经过优化以提高预测能力，而不是因果推理的能力。鉴于缺乏总体验证框架以及高维数据通常复杂的因果结构，目前，对高维调解的分析需要非常谨慎和努力，以纳入先验生物学知识。https://doi.org/10.1289/EHP6240。