Abstract

Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.org/10.1289/EHP5838.

Keywords

QuantileQuantile regressionStatisticsEconometricsInferenceRegressionConfoundingCausal inferenceRegression analysisLinear regressionComputationMathematicsComputer scienceAlgorithmArtificial intelligence

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Publication Info

Year
2020
Type
article
Volume
128
Issue
4
Pages
47004-47004
Citations
1374
Access
Closed

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Alexander P. Keil, Jessie P. Buckley, Katie M. O’Brien et al. (2020). A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures. Environmental Health Perspectives , 128 (4) , 47004-47004. https://doi.org/10.1289/ehp5838

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DOI
10.1289/ehp5838