Frequentist Grouped Weighted Quantile Sum Regression for Correlated Chemical Mixtures.

ABSTRACT: As individuals are exposed to a myriad of potentially harmful pollutants every day, it is important to determine which actors have the greatest influence on health outcomes. However, jointly modeling the associations of multiple pollutant exposures is often hindered by the presence of highly correlated chemicals originating from a common source. A popular approach to analyzing associations between a disease outcome and several highly correlated exposures is Weighted Quantile Sum Regression (WQSR) modeling. WQSR provides increased stability in estimating model parameters but requires data splitting to estimate individual and group effects of chemicals, which reduces the power of the approach. A recent Bayesian implementation of WQSR regression provides a model fitting procedure that avoids

SUBMITTER: Rud D 

PROVIDER: S-EPMC11987061 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature