Environmental health research aims to discover and understand the links between environmental exposure and disease and to inform the regulatory community so that society can be protected against cancer, birth defects, and other adverse health effects associated with chemical, industrial and other exposures. Statistical science has a critical role to play in terms of providing the appropriate tools to design and analyze the studies needed to address the questions of interest, as well as quantifying risks and characterizing uncertainty. Recent years have seen some dramatic changes in the way that environmental risk assessment is accomplished. One such change is a move away from a traditional reliance on toxicological studies in animals to incorporate more epidemiological data. This shift has been facilitated by scientific advances that now allow researchers to accurately characterize human exposures in a variety of settings, as well as to measure genetic and other biomarkers that reflect subtle health effects and variations in susceptibility. This article will use a high profile case study to highlight some of the challenging statistical issues arising from this shifting emphasis from animal based toxicology to environmental epidemiology in the risk assessment world. Among the topics to be discussed are the uses of biologically based models and biomarkers, as well as the role of Bayesian methods to characterize uncertainty due to population heterogeneity, unmeasured confounders, exposure measurement error and model uncertainty.
"Epidemiologically Based Environmental Risk Assessment." Statist. Sci. 18 (4) 466 - 480, November 2003. https://doi.org/10.1214/ss/1081443230