Skip to main content
Journal cover image

Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.

Publication ,  Journal Article
Song, Y; Zhou, X; Kang, J; Aung, MT; Zhang, M; Zhao, W; Needham, BL; Kardia, SLR; Liu, Y; Meeker, JD; Smith, JA; Mukherjee, B
Published in: Stat Med
November 30, 2021

We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data, and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be suboptimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, one with a Gaussian mixture prior that enables correlated mediator selection and the other with a Potts mixture prior that accounts for the correlation among active mediators in mediation analysis. We develop efficient sampling algorithms for both methods. Various simulations demonstrate that our methods enable effective identification of correlated active mediators, which could be missed by using existing methods that assume prior independence among active mediators. The proposed methods are applied to the LIFECODES birth cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) and identified new active mediators with important biological implications.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

November 30, 2021

Volume

40

Issue

27

Start / End Page

6038 / 6056

Location

England

Related Subject Headings

  • Statistics & Probability
  • Mediation Analysis
  • Humans
  • Bayes Theorem
  • Algorithms
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Song, Y., Zhou, X., Kang, J., Aung, M. T., Zhang, M., Zhao, W., … Mukherjee, B. (2021). Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators. Stat Med, 40(27), 6038–6056. https://doi.org/10.1002/sim.9168
Song, Yanyi, Xiang Zhou, Jian Kang, Max T. Aung, Min Zhang, Wei Zhao, Belinda L. Needham, et al. “Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.Stat Med 40, no. 27 (November 30, 2021): 6038–56. https://doi.org/10.1002/sim.9168.
Song Y, Zhou X, Kang J, Aung MT, Zhang M, Zhao W, et al. Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators. Stat Med. 2021 Nov 30;40(27):6038–56.
Song, Yanyi, et al. “Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.Stat Med, vol. 40, no. 27, Nov. 2021, pp. 6038–56. Pubmed, doi:10.1002/sim.9168.
Song Y, Zhou X, Kang J, Aung MT, Zhang M, Zhao W, Needham BL, Kardia SLR, Liu Y, Meeker JD, Smith JA, Mukherjee B. Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators. Stat Med. 2021 Nov 30;40(27):6038–6056.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

November 30, 2021

Volume

40

Issue

27

Start / End Page

6038 / 6056

Location

England

Related Subject Headings

  • Statistics & Probability
  • Mediation Analysis
  • Humans
  • Bayes Theorem
  • Algorithms
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
  • 0104 Statistics