A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses

James Y. Dai, Janet L. Stanford and Michael LeBlanc

Journal of the American Statistical Association, 2022, vol. 117, issue 537, 198-213

Abstract: Mediation analysis is of rising interest in epidemiologic studies and clinical trials. Among existing methods, the joint significance 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 genetic 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 well-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. Supplementary materials for this article are available online.

Date: 2022
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DOI: 10.1080/01621459.2020.1765785

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