ArticleStatistics in medicine2021
Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bayesian Image Mediation Analysis.Journal of the American Statistical Association · 2026Article
- BHMnet: Bayesian high-dimensional mediation analysis with network information integration for correlated mediators.Briefings in bioinformatics · 2026Article
- Bayesian Variable Selection for High-Dimensional Mediation Analysis: Application to Metabolomics Data in Epidemiological Studies.Statistics in medicine · 2026Article
- A Bayesian high-dimensional mediation analysis for multilevel genome-wide epigenetic data.Journal of applied statistics · 2025Article
- FABIO: TWAS fine-mapping to prioritize causal genes for binary traits.PLoS genetics · 2024Article
- Mediation analysis in longitudinal study with high-dimensional methylation mediators.Briefings in bioinformatics · 2024Article
- A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.Biostatistics (Oxford, England) · 2024Article
- Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology.Current environmental health reports · 2024Review
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons.Genetic epidemiology · 2023Review
- DNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors.Frontiers in cardiovascular medicine · 2022Article
- Discussion of "Statistical disease mapping for heterogeneous neuroimaging studies".The Canadian journal of statistics = Revue canadienne de statistique · 2021Article
- Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.Computational and structural biotechnology journal · 2021Review
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12 authors.
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Abstract
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.
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