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A multiple-testing procedure for high-dimensional mediation hypotheses.

James Y Dai1, Janet L Stanford1, Michael LeBlanc1

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109.

Journal of the American Statistical Association
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiple-testing procedure for high-dimensional mediation analysis, improving accuracy and power in epidemiological and clinical research. The method effectively controls error rates, enabling better detection of novel mediation relationships.

Keywords:
composite null hypothesisintersection-union testjoint significancemediation analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetics
  • Cancer Research

Background:

  • Mediation analysis is increasingly important in epidemiology and clinical trials.
  • Existing joint significance (JS) tests have limitations, including overly conservative type I error rates and low power for high-dimensional mediation hypotheses.

Purpose of the Study:

  • To develop a novel multiple-testing procedure for accurately controlling the family-wise error rate (FWER) and false discovery rate (FDR) in high-dimensional mediation analysis.
  • To enhance the power to detect mediation relationships in complex datasets.

Main Methods:

  • The procedure estimates proportions of component null hypotheses and the mixture null distribution of p-values.
  • It employs a multiple-testing framework to control FWER and FDR.
  • An R package, HDMT, was developed to implement the proposed procedure.

Main Results:

  • Theoretical developments and simulations confirm effective control of FWER and FDR.
  • The procedure demonstrated improved power to detect novel mediation relationships.
  • Applied to DNA methylation and prostate cancer research, it yielded well-behaved quantile-quantile plots.

Conclusions:

  • The proposed multiple-testing procedure offers an accurate and powerful approach for high-dimensional mediation analysis.
  • It successfully addresses limitations of existing methods, particularly in complex biological and medical research.
  • The freely accessible HDMT R package facilitates its application in practice.