Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose-Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose-Response Analysis
AffiliationUniv Arizona, Interdisciplinary Program Stat
Bayesian prior elicitation
quantal dose-response data
MetadataShow full item record
CitationWheeler, M. W., Piegorsch, W. W. and Bailer, A. J. (2019), Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis. Risk Analysis, 39: 616-629. doi:10.1111/risa.13218
RightsPublished 2018. This article is a U.S. Government work and is in the public domain in the USA.
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AbstractQuantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose-response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose-response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose-response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose-response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.
NotePublic domain article
VersionFinal published version
SponsorsU.S. National Institutes of Health [R03-ES027394]; National Science Foundation [CCF-1740858]; NIOSH
Except where otherwise noted, this item's license is described as Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
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