Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

• Published in: Environmental health Vol. 17; no. 1; pp. 67 - 10
• Main Authors: Bobb, Jennifer F., Claus Henn, Birgit, Valeri, Linda, Coull, Brent A.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.08.2018; BioMed Central; BMC
• Subjects: Air pollution; Bayes Theorem; Bayesian analysis; Computer programs; Continuity (mathematics); Dose-response effects; Environmental Exposure - adverse effects; Environmental health; Environmental Health - methods; Environmental Monitoring - methods; Environmental Pollutants - adverse effects; Epidemiology; Estimation; Exposure; Exposure-response; Gaussian process; Health; Health aspects; Health risk estimation; Medical research; Methodology; Mixtures; Models, Statistical; Multiple exposures; Nonlinear response; Open source software; Organic chemicals; Pollutants; Pollution; Principal components analysis; Programming languages; Regression analysis; Response functions; Risk analysis; Risk factors; Simulation; Software; Source code; Statistical analysis; Statistical methods; Statistical software; Variable selection; Exposure-response; Health risk estimation; Mixtures; Multiple exposures; Variable selection

Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-r...