Bayesian multiple index models for environmental mixtures. Issue 1 (12th October 2021)

• Record Type: Journal Article
• Authors: McGee, Glen; Wilson, Ander; Webster, Thomas F.; Coull, Brent A.
• Is Part Of: Biometrics. Volume 79:Issue 1(2023)
• Page Start: 462
• Page End: 474
• Publication Date: 2021-10-12
• Subjects: environmental health -- kernel machine regression -- multiple index models -- variable selection; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13569
• Languages: English
• ISSNs: 0006-341X

Abstract

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.