Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods, the joint significance (JS) test yields an overly conservative type I error rate and low power, particularly for high-dimensional mediation hypotheses. In this article we develop a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of component null hypotheses and the underlying mixture null distribution of p-values. Theoretical developments and simulation experiments prove that the proposed procedure effectively controls FWER and FDR. Two mediation analyses on DNA methylation and cancer research are presented: assessing the mediation role of DNA methylation in genLetic regulation of gene expression in primary prostate cancer samples; exploring the possibility of DNA methylation mediating the effect of exercise on prostate cancer progression. Results of data examples include wellL-behaved quantile-quantile plots and improved power to detect novel mediation relationships. An R package HDMT implementing the proposed procedure is freely accessible in CRAN.

中介分析在流行病学和临床试验中的兴趣日益浓厚。在现有方法中，联合重要性（JS）检验会产生过于保守的I型错误率和较低的功效，尤其是对于高维调解假设而言。在本文中，我们开发了一种多重测试程序，可以在测试高维调解假设时准确地控制家庭错误率（FWER）和错误发现率（FDR）。我们程序的核心是基于估计分量零假设的比例和p值的基础混合零分布。理论发展和仿真实验证明，该程序有效地控制了FWER和FDR。提出了两种关于DNA甲基化和癌症研究的中介分析：评估DNA甲基化在遗传调控原发性前列腺癌样本中基因表达的介导作用；探索DNA甲基化介导运动对前列腺癌进展的影响的可能性。数据示例的结果包括行为良好的分位数-分位数图和提高的检测新型调解关系的能力。可以在CRAN中免费访问实现建议的过程的R包HDMT。