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Article summary:
1. This paper introduces an open-source software package in the R programming language, the bkmr R package, for estimating the health effects of multi-pollutant mixtures.
2. The Bayesian kernel machine regression (BKMR) method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures.
3. The methods are illustrated using fully reproducible examples with the provided R code and demonstrate the ability to estimate overall, single-exposure, and interactive health effects.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about a new statistical software package for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. The authors provide clear explanations of their methodology and results, as well as reproducible examples with accompanying R code to illustrate their methods. Additionally, they discuss potential limitations of their approach such as high-dimensional vectors of exposures leading to poorly fitting regression models or highly correlated exposures leading to difficulty in variable selection.
However, there are some potential biases that should be noted when considering this article. For example, while the authors do discuss potential limitations of their approach, they do not explore any counterarguments or alternative approaches that could be used instead of BKMR for analyzing multi-pollutant mixtures. Additionally, there is no discussion of possible risks associated with using this software package or any potential ethical considerations that should be taken into account when using it. Furthermore, while the authors provide detailed information about their methodology and results, they do not provide any evidence to support their claims or discuss any unexplored points of consideration that may have been relevant to their study but were not addressed due to time or resource constraints.