ArticleStatistics in medicine2020
Group testing in mediation analysis.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Causal Relationships Between Plasma Metabolites, Inflammatory Factors, and Oral Cancer Risk: A Comprehensive Mendelian Randomization Study With Mediation Analysis.Human mutation · 2026Article
- Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.Biostatistics (Oxford, England) · 2025Article
- Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025Article
- A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia.PLoS genetics · 2024Article
- Speeding up interval estimation forbioRxiv : the preprint server for biology · 2024Article
- Mediation analysis using incomplete information from publicly available data sources.Statistics in medicine · 2024Article
- A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia.bioRxiv : the preprint server for biology · 2024Article
- Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Article
- Mediation analysis for survival data with high-dimensional mediators.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women.
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What OpenQuestion holds
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.