June 2024 MASH: Mediation analysis of survival outcome and high-dimensional omics mediators with application to complex diseases
Sunyi Chi, Christopher R. Flowers, Ziyi Li, Xuelin Huang, Peng Wei
Author Affiliations +
Ann. Appl. Stat. 18(2): 1360-1377 (June 2024). DOI: 10.1214/23-AOAS1838


Environmental exposures, such as cigarette smoking, influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH), based on a second-moment-based measure of total mediation effect for survival data analogous to the R2 measure in a linear model. In addition, we propose a three-step mediator selection procedure to mitigate potential bias induced by nonmediators. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

Funding Statement

This work was supported by the National Institutes of Health (NIH) grants R01HL116720.
P.W. was partially supported by NIH grant P50CA217674 and Cancer Prevention and Research Institute of Texas (CPRIT) Grant RP230166.
X.H. was partially supported by NIH grant R01CA272806, U54CA096300, U01CA152958, and P50CA100632 and the Dr. Mien-Chie Hung and Mrs. Kinglan Hung Endowed Professorship.
Drs. C.R.F. and X.H. were partially supported by the grant for Recruitment of Established Investigators from the Cancer Prevention & Research Institute of Texas (CPRIT, RR190079, PI-Christopher Flowers).
The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195).


The authors acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC resources. The authors declare that there are no conflicts of interest. This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI. The manuscript was edited by Don Norwood and Sarah Bronson, ELS, of Editing Services, Research Medical Library at The University of Texas MD Anderson Cancer Center. The authors thank two anonymous reviewers, an Associate Editor, and Editor, Dr. Beth Ann Griffin, for their many constructive comments which helped improve the manuscript.


Download Citation

Sunyi Chi. Christopher R. Flowers. Ziyi Li. Xuelin Huang. Peng Wei. "MASH: Mediation analysis of survival outcome and high-dimensional omics mediators with application to complex diseases." Ann. Appl. Stat. 18 (2) 1360 - 1377, June 2024. https://doi.org/10.1214/23-AOAS1838


Received: 1 August 2022; Revised: 1 August 2023; Published: June 2024
First available in Project Euclid: 5 April 2024

Digital Object Identifier: 10.1214/23-AOAS1838

Keywords: High-dimensional mediators , mediation analysis , Survival analysis , total mediation effect , Variable selection

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.18 • No. 2 • June 2024
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