ArticleBiometrika2020
Estimation and inference for the indirect effect in high-dimensional linear mediation models.
Article in Biometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Doubly robust evaluation of high-dimensional surrogate markers.Biostatistics (Oxford, England) · 2023Trial
- Deconfounded-Debiased Estimation and Inference for High-Dimensional Mediation Analysis With Pervasive Hidden Confounders.Statistics in medicine · 2026Article
- Physical Fitness Is Negatively Associated With DNA Methylation-Based Risk of Aging-Related Diseases.Aging cell · 2026Article
- Benchmarking large language models for predictive modeling in biomedical research with a focus on reproductive health.Cell reports. Medicine · 2026Article
- High-dimensional causal mediation analysis by partial sum statistic and sample splitting strategy in imaging genetics application.Bioinformatics (Oxford, England) · 2025Article
- High-dimensional mediation analysis for longitudinal mediators and survival outcomes.Briefings in bioinformatics · 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
- Mediation Analysis with Multiple Exposures and Multiple Mediators.Statistics in medicine · 2024Article
- Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons.PLoS genetics · 2023Article
- Genome-wide multimediator analyses using the generalized Berk-Jones statistics with the composite test.Bioinformatics (Oxford, England) · 2023Article
- Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic.Journal of econometrics · 2023Article
- HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data.BMC bioinformatics · 2022Article
- Estimation of the proportion of treatment effect explained by a high-dimensional surrogate.Statistics in medicine · 2022Article
- Varying Coefficient Mediation Model and Application to Analysis of Behavioral Economics Data.Journal of business & economic statistics : a publication of the American Statistical Association · 2022Article
- Heterogeneous Mediation Analysis on Epigenomic PTSD and Traumatic Stress in a Predominantly African American Cohort.Journal of the American Statistical Association · 2022Article
- Evaluating multiple surrogate markers with censored data.Biometrics · 2021Article
- Mediation analysis for survival data with high-dimensional mediators.Bioinformatics (Oxford, England) · 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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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.
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