Incorporating regulatory guideline values in analysis of epidemiology data.

ABSTRACT: Fundamental to regulatory guidelines is to identify chemicals that are implicated with adverse human health effects and inform public health risk assessors about "acceptable ranges" of such environmental exposures (e.g., from consumer products and pesticides). The process is made more difficult when accounting for complex human exposures to multiple environmental chemicals. Herein we propose a new class of nonlinear statistical models for human data that incorporate and evaluate regulatory guideline values into analyses of health effects of exposure to chemical mixtures using so-called 'desirability functions' (DFs). The DFs are incorporated into nonlinear regression models to allow for the simultaneous estimation of points of departure for risk assessment of combinations of individual sub

SUBMITTER: Gennings C 

PROVIDER: S-EPMC6261378 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature