ArticleBiometrics2020
Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Doubly robust evaluation of high-dimensional surrogate markers.Biostatistics (Oxford, England) · 2023Trial
- 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
- Microbiome mediation analysis: methods, assumptions, and practical considerations.Frontiers in cellular and infection microbiology · 2026Review
- Bayesian Variable Selection for High-Dimensional Mediation Analysis: Application to Metabolomics Data in Epidemiological Studies.Statistics in medicine · 2026Article
- Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.Biostatistics (Oxford, England) · 2025Article
- Mediation with External Summary Statistic Information.Biostatistics (Oxford, England) · 2025Article
- High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.Bioinformatics (Oxford, England) · 2025Article
- Causal mediation analysis: selection with asymptotically valid inference.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Debiased machine learning for ultra-high dimensional mediation analysis.Bioinformatics (Oxford, England) · 2025Article
- High-dimensional mediation analysis reveals the mediating role of physical activity patterns in genetic pathways leading to AD-like brain atrophy.BioData mining · 2025Article
- Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD.bioRxiv : the preprint server for biology · 2025Article
- Neighborhood environment associations with cognitive function and structural brain measures in older African Americans.BMC medicine · 2025Article
- 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 with Multiple Exposures and Multiple Mediators.Statistics in medicine · 2024Article
- Article
- Speeding up interval estimation forbioRxiv : the preprint server for biology · 2024Article
- Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.Environment international · 2024Article
- A Bayesian nonparametric approach for multiple mediators with applications in mental health studies.Biostatistics (Oxford, England) · 2024Article
Corrections and comments
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10 authors.
Funding
Abstract
Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of -omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi-Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.