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
• ISSN: 1476-069X; 1476-069X
• DOI: 10.1186/s12940-018-0413-y

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-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.