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

作者：Bobb JF, Claus Henn B, Valeri L, Coull BA · 发表于：Environmental health : a global access science source · 年份：2018 · DOI：10.1186/s12940-018-0413-y · 研究领域：Exposure-response、Health risk estimation、Mixtures、Multiple exposures、Variable selection

BACKGROUND: 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. METHODS: 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. RESULTS: Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate ...