ArticleEnvironmental health perspectives2016
A Systematic Comparison of Linear Regression-Based Statistical Methods to Assess Exposome-Health Associations.
Article in Environmental health perspectives, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 3 of them syntheses that pooled it.
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100 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Design and methodology challenges of environment-wide association studies: A systematic review.Environmental research · 2020Pooled it
- Assessing environmental exposures in children: toward a comprehensive exposome approach.Pediatric research · 2026Review
- Large-scale assessment of socioeconomic, demographic and health system structures with US county excess mortality, 2020-2024.medRxiv : the preprint server for health sciences · 2026Article
- Untargeted metabolomic profiling of childhood asthma: An exploratory analysis of anthropogenic chemicals and the serum metabolome.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Relevance and challenges of exposome studies for environmental health research.Environmental health : a global access science source · 2026Review
- Per- and Polyfluoroalkyl Substance Exposure Associated with Later Puberty in Norwegian Girls: Data from the Bergen Growth Study 2.Environment & health (Washington, D.C.) · 2026Article
- Associations of metal mixtures with insulin resistance and glucose homeostasis in U.S. adults from the NHANES 2011-2018.Scientific reports · 2026Article
- Expert perspectives on exposure-response functions for urban health policy: Lessons from a UBDPolicy workshop.Environmental research · 2026Review
- In utero air pollution exposure and pre- and postnatal brain development: a review.Neuroscience applied · 2026Review
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- Collapsible Kernel Machine Regression for Exposomic Analyses.Statistics in medicine · 2025Article
- TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.The annals of applied statistics · 2025Article
- External Exposome Factors and Adverse Heart Failure Outcomes in the OneFlorida+ Network: Retrospective Cohort Study.JMIR formative research · 2025Article
- Using mixture and exposome methods to assess the associations and interactions of chemical biomarkers with semen parameters.Environment international · 2025Article
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- Statistical Methods for Chemical Mixtures: A Roadmap for Practitioners Using Simulation Studies and a Sample Data Analysis in the PROTECT Cohort.Environmental health perspectives · 2025Article
- Long-Term Exposure to Traffic-Related Air Pollution and Noise and Dynamic Brain Connectivity across Adolescence.Environmental health perspectives · 2025Article
- Mapping Source-Specific Air Pollution Exposures Using Positive Matrix Factorization Applied to Multipollutant Mobile Monitoring in Seattle, WA.Environmental science & technology · 2025Article
40 more citing papers are in PubMed but not listed here.
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14 authors.
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Abstract
backgroundThe exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.
objectivesWe compared the performances of linear regression-based statistical methods in assessing exposome-health associations.
methodsIn a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.
resultsOn average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.
conclusionsCorrelation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods. Citation: Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124:1848-1856; http://dx.doi.org/10.1289/EHP172.
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