A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses. Issue 537 (2nd January 2022)

• Record Type: Journal Article
• Authors: Dai, James Y.; Stanford, Janet L.; LeBlanc, Michael
• Is Part Of: Journal of the American Statistical Association. Volume 117:Issue 537(2022)
• Page Start: 198
• Page End: 213
• Publication Date: 2022-01-02
• Subjects: Composite null hypothesis -- Intersection-union test -- Joint significance -- Mediation analysis; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2020.1765785
• Languages: English
• ISSNs: 0162-1459

Abstract

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.