Selecting Experimental Data for Use in Quantitative Risk Assessment: An Expert-Judgment Approach

Abstract

In many cancer risk assessments the experimental data used in statistical modeling are selected by applying generic guidelines. The guidelines exclude use of some types of experimental data and often appear arbitrary since rules rather than scientific judgments guide selection of data. This paper implements an alternative approach in which data are selected based on the judgments of practicing scientists. Eight such scientists were identified through an explicit selection procedure to help select data for use in a dose-response assessment of formaldehyde. Judgments about appropriate data sets were then elicited in personal interviews using a formal interview protocol. Appropriate data sets were fit to the multistage model and used as the basis for low-dose extrapolation. Low-dose risk estimates are shown to be sensitive to the selection of data, especially the treatment of benign tumors. The recommendations of the experts also differ in some respects from the choices made in previously published risk assessments. This suggests that scientific judgment may be an appropriate method to augment guidelines when a broad range of data is available. The paper argues that the expert judgment approach has some advantages that are worth considering.