ArticleBiometrics2023
Bayesian multiple index models for environmental mixtures.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Everything All at Once: On Choosing an Estimand for Multi-component Environmental Exposures.Epidemiology (Cambridge, Mass.) · 2026Article
- Workflow for Statistical Analysis of Environmental Mixtures.Environmental health perspectives · 2026Review
- Multiple-index interaction models to accommodate exposure grouping in environmental mixtures.Biometrics · 2026Article
- Distributed lag interaction model with index modification.Biostatistics (Oxford, England) · 2025Article
- Collapsible Kernel Machine Regression for Exposomic Analyses.Statistics in medicine · 2025Article
- Partial Effects in Environmental Mixtures: Evidence and Guidance on Methods and Implications.Environmental health perspectives · 2025Article
- Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository.medRxiv : the preprint server for health sciences · 2024Article
- Partial-linear single-index Cox regression models with multiple time-dependent covariates.BMC medical research methodology · 2024Article
- Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence.Communications biology · 2024Article
- Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood.The Science of the total environment · 2024Article
- Article
- The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform.Frontiers in public health · 2024Article
- Constrained groupwise additive index models.Biostatistics (Oxford, England) · 2023Article
- A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships.Journal of agricultural, biological, and environmental statistics · 2023Article
- Metals and per- and polyfluoroalkyl substances mixtures and birth outcomes in the New Hampshire Birth Cohort Study: Beyond single-class mixture approaches.Chemosphere · 2023Article
- Article
- Methodological Challenges in Spatial and Contextual Exposome-Health Studies.Critical reviews in environmental science and technology · 2023Article
- Principal Component Pursuit for Pattern Identification in Environmental Mixtures.Environmental health perspectives · 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
4 authors.
Funding
Abstract
An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results.
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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.