A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures Article Swipe

PDF

Related Concepts

quantile quantile regression statistics econometrics inference regression confounding causal inference regression analysis linear regression computation mathematics computer science algorithm artificial intelligence

Alexander P. Keil , Jessie P. Buckley , Katie M. O’Brien , Kelly K. Ferguson , Shanshan Zhao , Alexandra J. White ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.1289/ehp5838 · OA: W2918173153

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.