ArticleBiometrics2024
Controlling false discovery rate for mediator selection in high-dimensional data.
Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Differences in cognitive performance and neuroanatomy according to Alzheimer's disease pathophysiology.Arquivos de neuro-psiquiatria · 2026Article
- Disrupted prefrontal-sensorimotor functional connectivity mediates the association between depression and symptom severity in patients with irritable bowel syndrome.Frontiers in human neuroscience · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.
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Registered trials
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