Multivariate left-censored Bayesian model for predicting exposure using multiple chemical predictors.

ABSTRACT: Environmental health exposures to airborne chemicals often originate from chemical mixtures. Environmental health professionals may be interested in assessing exposure to one or more of the chemicals in these mixtures, but often exposure measurement data are not available, either because measurements were not collected/assessed for all exposure scenarios of interest or because some of the measurements were below the analytical methods' limits of detection (i.e. censored). In some cases, based on chemical laws, two or more components may have linear relationships with one another, whether in a single or in multiple mixtures. Although bivariate analyses can be used if the correlation is high, often correlations are low. To serve this need, this paper develops a multivariate framework for ass

SUBMITTER: Groth C 

PROVIDER: S-EPMC6241297 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature