Comprehensive Realism's Weight-of-Evidence Based Distributional Dose-Response Characterization

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Abstract
Challenges to low-dose linearity and other default assumptions in cancer risk assessment and the limitations associated with NOAELs, LOAELs, and constant uncertainty factor values in the evaluation of noncancer health effects have stimulated the continued evolution of risk assessment methodologies. The increasing need for more realistic estimates of the dose-response relationship, better uncertainty characterization, and greater utilization of cost-benefit analyses have also contributed to this evolution. “Comprehensive Realism” is an emerging quantitative weight-of-evidence based risk assessment methodology for both cancer and noncancer health effects which utilizes probability distributions and decision analysis techniques to reflect more of the relevant human exposure data, more of the available and pertinent human and animal dose-response data, and the current state of knowledge about the relative plausibility of alternative dose-response analyses. A tree (like a decision tree and a probability tree) is used to decompose the dose-response assessment into component factors, to provide a structure for explicitly considering multiple alternatives for each factor, and to explicitly incorporate the current state of knowledge about the relative plausibility of these alternatives. Groundbreaking work has demonstrated the feasibility of weight-of-evidence based distributional characterizations, and provided initial examples. Computer software implementations are also available.