Uncertainty factors

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Abstract: The practice of Uncertainty Factors as applied to non-cancer endpoints in the IRIS data base harkens back to traditional safety factors. In the era before risk quantification these were used to build in a ‘margin of safety’. As risk quantification takes hold, the safety factor methods yield to quantitative risk calculations to guarantee safety. Many authors believe that uncertainty factors can be given a probabilistic interpretation as ratios of response rates, and that the reference values computed according to the IRIS methodology can thus be converted to random variables whose distributions can be computed with Monte Carlo methods, based on the distributions of the uncertainty factors. Recent proposals from the National Research Council echo this view. Based on probabilistic arguments, several authors claim that the current practice of uncertainty factors is over-protective. When interpreted probabilistically, uncertainty factors entail very strong assumptions on the underlying response rates. For example, the factor for extrapolating from animal to human is the same whether the dosage is chronic or sub-chronic. Together with independence assumptions, these assumptions entail that the covariance matrix of the logged response rates is singular. In other words, the accumulated assumptions entail a log linear dependence between the response rates. This in turn means that any uncertainty analysis based on these assumptions is ill-conditioned; it effectively computes uncertainty conditional on a set of zero probability. The practice of uncertainty factors is due for a thorough review. Two directions are briefly sketched, one based on standard regression models, and one based on non-parametric continuous Bayesian Belief Nets.

Keywords: Uncertainty factors, LOAEL, NOAEL, Benchmark Dose


The full article can be found here [1]