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Bayesian sparse mediation analysis with targeted penalization of natural indirect effects

Author

Listed:
  • 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
  • Bhramar Mukherjee

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.

Suggested Citation

  • 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 & Bhramar Mukherjee, 2021. "Bayesian sparse mediation analysis with targeted penalization of natural indirect effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1391-1412, November.
  • Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1391-1412
    DOI: 10.1111/rssc.12518
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    References listed on IDEAS

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    1. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    2. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    3. Yang Ni & Francesco C. Stingo & Veerabhadran Baladandayuthapani, 2019. "Bayesian Graphical Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 184-197, January.
    4. Yanyi Song & Xiang Zhou & Min Zhang & Wei Zhao & Yongmei Liu & Sharon L. R. Kardia & Ana V. Diez Roux & Belinda L. Needham & Jennifer A. Smith & Bhramar Mukherjee, 2020. "Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies," Biometrics, The International Biometric Society, vol. 76(3), pages 700-710, September.
    5. Max T. Aung & Yanyi Song & Kelly K. Ferguson & David E. Cantonwine & Lixia Zeng & Thomas F. McElrath & Subramaniam Pennathur & John D. Meeker & Bhramar Mukherjee, 2020. "Application of an analytical framework for multivariate mediation analysis of environmental data," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    6. Yen-Tsung Huang & Wen-Chi Pan, 2016. "Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators," Biometrics, The International Biometric Society, vol. 72(2), pages 402-413, June.
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    Cited by:

    1. Lulu Shang & Wei Zhao & Yi Zhe Wang & Zheng Li & Jerome J. Choi & Minjung Kho & Thomas H. Mosley & Sharon L. R. Kardia & Jennifer A. Smith & Xiang Zhou, 2023. "meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Caubet, Miguel & Samoilenko, Mariia & Drouin, Simon & Sinnett, Daniel & Krajinovic, Maja & Laverdière, Caroline & Marcil, Valérie & Lefebvre, Geneviève, 2023. "Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator: Exploring the role of obesity in the association between cranial radiation therapy for childhood acut," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

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