ArticleComputational statistics & data analysis2020
Sparse Principal Component based High-Dimensional Mediation Analysis.
Article in Computational statistics & data analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Sparse CCA-based mediation analysis with high-dimensional exposures and mediators.Bioinformatics (Oxford, England) · 2026Article
- Long-read assembly reveals vast transcriptional complexity in the placenta associated with metabolic and endocrine function.Nature communications · 2026Article
- Bayesian Image Mediation Analysis.Journal of the American Statistical Association · 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
- Mediation analysis with graph mediator.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
- CRAmed: a conditional randomization test for high-dimensional mediation analysis in sparse microbiome data.Bioinformatics (Oxford, England) · 2025Article
- Mediation Analysis with Multiple Exposures and Multiple Mediators.Statistics in medicine · 2024Article
- Mediation analysis in longitudinal study with high-dimensional methylation mediators.Briefings in bioinformatics · 2024Article
- Network-Assisted Mediation Analysis with High-Dimensional Neuroimaging Mediators.Proceedings of machine learning research · 2024Article
- A new framework for exploratory network mediator analysis in omics data.Genome research · 2024Article
- Model Selection for Exposure-Mediator Interaction.Data science in science · 2024Article
- Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons.PLoS genetics · 2023Article
- Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.Computational statistics & data analysis · 2023Article
- Article
- A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.Bioinformatics (Oxford, England) · 2023Article
- On optimal two-stage testing of multiple mediators.Biometrical journal. Biometrische Zeitschrift · 2022Article
- HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
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Registered trials
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.