Probabilistic Characterization of Missing Data in Noncancer Risk Assessment. S. J. S. Baird, 87 Rockland Place, Newton, MA 02164; A. I. Shlyakhter, Harvard University, Department of Physics, Cambridge, MA 02138; and J. S. Evans, Harvard School of Public Health, Department of Environmental Health, 665 Huntington Ave., Building I-1308, Boston, MA 02115
We have developed a new approach for characterizing the uncertainty in the population threshold dose (Baird et al., Human and Ecological Risk Assessment, 1996, 2:79-102) where uncertainty factors are represented as probability distributions instead of point estimates. Here we describe approaches for the probabilistic characterization of the Data Quantity/Quality factor that accounts for missing bioassay data. Noncancer risk assessment methods rely on the No-Observed-Adverse-Effect Level (NOAEL) of the most sensitive or critical effect. Thus, all possible effects need to be studied to be certain that the true critical effect is identified. A complete dataset commonly includes a rat chronic, mouse chronic, rat developmental and rat reproductive bioassays. However, frequently a complete dataset is not available for the compound of interest. In this case, regulatory agencies have accounted for the chance that the missing studies would have yielded a lower NOAEL by using an point estimate uncertainty factor, often called a Data Quantity/Quality factor. To fully account for the uncertainty in the population threshold, the uncertainty in how much lower the missing NOAEL(s) might be should be characterized probabilistically. Using the complete datasets for 35 pesticides, we developed 3 approaches for estimating the probability distribution of the ratio of the estimate of the missing NOAEL to the true minimum NOAEL. Because the missing NOAEL is only important if it is lower than the existing NOAELs, the estimate from each approach only replaces the existing NOAEL if it is lower. The three complimentary approaches include a simple method, taking the ratios of the minimum of the available bioassays and the minimum of the complete set of bioassays, the true NOAEL; a standard regression method, regressing the minimum of the available studies on the true NOAEL; and a constrained regression method, where a penalty term is added to the regression equation to decrease the likelihood that the estimated NOAEL is greater than the available NOAEL. The advantages and limitations of each approach will be discussed in relation to risk assessment and risk management goals.
Work supported by Health Canada, under Contract No. 5366.