EconPapers    
Economics at your fingertips  
 

Bayesian sparse mediation analysis with targeted penalization of natural indirect effects

Yanyi Song, Xiang Zhou, Jian Kang, Max T. Aung, Min Zhang, Wei Zhao, Belinda L. Needham, Sharon L. R. Kardia, Yongmei Liu, John D. Meeker, Jennifer A. Smith and Bhramar Mukherjee

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 5, 1391-1412

Abstract: Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high‐dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high‐dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure‐mediator effect and mediator‐outcome effect with either (a) a four‐component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modelling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four‐component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in‐depth analysis of two ongoing epidemiologic studies: the Multi‐Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.

Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/rssc.12518

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:70:y:2021:i:5:p:1391-1412

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2021-11-19
Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1391-1412