ArticleEnvironmental health perspectives2018
Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures.
Article in Environmental health perspectives, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 4 of them syntheses that pooled it.
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Who cites it
93 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Exposure to per- and polyfluoroalkyl substances and inflammatory bowel disease: review and meta-analysis.Journal of exposure science & environmental epidemiology · 2026Pooled it
- Systematic review and meta-analysis of birth weight and PFNA exposures.Environmental research · 2023Pooled it
- Phthalate exposure and metabolic effects: a systematic review of the human epidemiological evidence.Environment international · 2019Pooled it
- Effects of Polybrominated Diphenyl Ethers on Child Cognitive, Behavioral, and Motor Development.International journal of environmental research and public health · 2018Pooled it
- Coexposure confounding and amplification of bias-an exploration with practical interpretation and perspectives.American journal of epidemiology · 2026Article
- Improving Inference in Air Pollution Epidemiology: The Case for Rethinking Multipollutant Adjustment.Epidemiology (Cambridge, Mass.) · 2026Article
- Mixture Toxicity in Human Health: Integrating One Health, Exposomics, and Modern Risk Assessment Strategies.Chemical research in toxicology · 2026Review
- Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026Review
- Association between greenspace exposure before, during, and after pregnancy and autism spectrum disorder in offspring.Journal of exposure science & environmental epidemiology · 2026Article
- 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
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- Prenatal and Early Postnatal Lead Exposure, Sensitive Periods, and Later Adult Mental Health.JAMA psychiatry · 2025Article
- Global high-resolution ultrafine particle number concentrations through data fusion with machine learning.Scientific data · 2025Article
- Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Impact of plastic-related chemicals on emotional and behavioral health in children from Poland.Environmental health : a global access science source · 2025Article
- Risks of Birth Defects Following In Utero Exposures to Unregulated Brominated Haloacetic Acids.Birth defects research · 2025Article
- Cohort Evidence Links Phthalate Exposure to Early Onset of Puberty in Chinese Girls.Environment & health (Washington, D.C.) · 2025Article
- Integrated chemical exposome-metabolome profiling of follicular fluid and associations with fertility outcomes during assisted reproduction.Environment international · 2025Article
- Human milk oligosaccharides and Campylobacter jejuni and Campylobacter coli gastroenteritis risk in a Nicaraguan Birth Cohort.The American journal of clinical nutrition · 2025Article
- Using mixture and exposome methods to assess the associations and interactions of chemical biomarkers with semen parameters.Environment international · 2025Article
33 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe analysis of health effects of exposure to mixtures is a critically important issue in human epidemiology, and increasing effort is being devoted to developing methods for this problem. A key feature of environmental mixtures is that some components can be highly correlated, raising the issues of confounding by coexposure and colinearity. A relatively unexplored topic in epidemiologic analysis of mixtures is the impact of residual confounding bias due to unmeasured or unknown variables.
objectivesThis paper examines the potential amplification of such biases when correlated exposure variables are included in regression models.
methodsWe use directed acyclic graphs (DAGs) to describe different simple scenarios involving residual confounding. We derive expressions for the expected value of the resulting bias using linear models and multiple linear regression.
resultsApproaches to the analysis of mixtures that involve regressing the outcome on several exposures simultaneously can in some cases amplify rather than reduce confounding bias. DISCUSSIONS: The problem of bias amplification can worsen with stronger correlation between mixture components or when more mixture components are included in the model.
conclusionsInvestigators must consider steps to minimize possible bias amplification in the design and analysis of epidemiologic studies of multiple correlated exposures. This may be particularly important when biomarkers of exposure are used. https://doi.org/10.1289/EHP2450.
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