It has been almost 14 years since The National Academies of Sciences, Engineering, and Medicine 2009 report “Science and Decisions” recommended applying probabilistic methods to cancer dose-response assessment in order to better address uncertainty and variability.
Our very own Weihsueh Chiu and Kan Shao, as well as Suji Jang, recently collaborated to combine Bayesian Model Averaging-based Benchmark Dose modeling and probabilistic extrapolation methods developed by the World Health Organization to predict both overall population risk and the population variability in individual risks. In most cases, traditional cancer slope factors are within the confidence bound of probabilistic estimates of population risk, but are not adequately protective at 95% confidence. In a few (< 1 in 10) cases of highly non-linear dose-response, however, traditional cancer slope factors are > 10-fold overprotective in light of probabilistic predictions. They concluded that application of Bayesian and probabilistic methods in cancer dose-response, as recommended by “Science and Decisions,” leads to more scientifically rigorous basis for cancer risk assessment by more fully incorporating information from dose-response data while incorporating multiple sources of uncertainty and variability. Read more at https://doi.org/10.1016/j.envint.2023.107959