ArticleJournal of the American Statistical Association2022
A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Subgroup analyses and effect modification with Bayesian kernel machine regression.American journal of epidemiology · 2026Article
- Urinary and circulatory levels of plasticizer metabolites and their associations with renal function: a cross-sectional analysis of the NHANES cohort.Annals of medicine · 2025Article
- LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES.The annals of applied statistics · 2025Article
- Association between exposure to a mixture of organochlorine pesticides and hyperuricemia in U.S. adults: A comparison of four statistical models.Eco-Environment & Health · 2024Article
- Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood.The Science of the total environment · 2024Article
- A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.Frontiers in public health · 2024Review
- Joint associations among prenatal metal mixtures and nutritional factors on birth weight z-score: Evidence from an urban U.S. population.Environmental research · 2022Article
- Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.International journal of environmental research and public health · 2022Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work we address this problem by proposing a Cross-validated Ensemble of Kernels (CVEK) that learns the space of appropriate functions for the main effects using a cross-validated ensemble approach. With a carefully chosen library of base kernels, CVEK flexibly estimates the form of the main-effect functions from the data, and encourages test power by guarding against over-fitting under the alternative. The method is motivated by a study on the interaction between metal exposures
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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.