High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.

中文翻译：

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高通量数据的中介分析方法综述

高通量技术使得高维设置变得越来越普遍，为高维中介方法的发展提供了机会。我们的目的是通过总结和讨论高维中介分析的最新进展，为使用高维中介分析的研究人员提供有用的指导，并为生物统计学家开发高维中介分析的思路提供有用的指导。当将单中介和多中介分析扩展到高维环境时，该方法仍然面临许多挑战。高维中介方法的发展试图解决这些问题，例如筛选真正的中介变量、通过变量选择估计中介效应、降低中介维度以解决变量之间的相关性以及利用复合零假设检验来检验它们。尽管这些高维中介问题已经得到一定程度的解决，但仍然存在一些挑战。首先，在选择中介变量时很少考虑中介变量之间的相关性。其次，在不结合先前的生物学知识的情况下缩小规模会使结果难以解释。此外，目前还缺乏针对高维中介分析中严格序贯可忽略性假设的敏感性分析方法。分析师在使用每种方法时需要考虑它们的适用性，而生物统计学家可以考虑方法的扩展和改进。