A Unified Probabilistic Framework for Cancer Risk Management

Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment

Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10-6 population incidence were typically 3- to 30-fold lower than traditional slope factors. However, in a small (<7%) number of cases of highly non-linear experimental dose-response, the probabilistic RSDs were >10-fold less stringent. Probabilistic RSDs were also protective of individual risks of 10-4 in >99% of the population. We conclude that implementing Bayesian and probabilistic methods provides a more scientifically rigorous basis for cancer dose-response assessment and thereby improves overall cancer risk characterization.

Introduction to Special Issue on Risk Assessment, Economic Evaluation, and Decisions

Integrating risk assessment, economic evaluation, and uncertainty to inform policy decisions is a core challenge to risk analysis. In September 2019, the Harvard Center for Risk Analysis, with support from the Society for Risk Analysis Economics and Benefits Analysis Specialty Group and others, convened a workshop to address this issue. The workshop built in part on the recommendations of the 2009 National Research Council report, Science and Decisions: Advancing Risk Assessment. It honored John S. Evans, whose thoughtful and innovative teaching and scholarship have significantly advanced thinking on these issues. This special issue features a profile of Dr. Evans and nine articles that build on work presented at the workshop.