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

• Published in: Environmental health perspectives Vol. 128; no. 4; p. 47004
• Main Authors: Keil, Alexander P., Buckley, Jessie P., O’Brien, Katie M., Ferguson, Kelly K., Zhao, Shanshan, White, Alexandra J.
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences 01.04.2020; Environmental Health Perspectives
• Subjects: Air pollution control; Bias; Coal fired power plants; Complex Mixtures - adverse effects; Computation; Computer simulation; Confidence intervals; Environmental Exposure - analysis; Environmental Pollutants - adverse effects; Epidemiology; Exposure; Generalized linear models; Humans; Hypothesis testing; Industrial emissions; Linear Models; Methods; Mining industry; Nonlinear systems; Pollutants; Pollution control; Prejudice; Public health; Public health movements; Regression Analysis; Statistical analysis; North Carolina

Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint...