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

Wheeler, MW; Piegorsch, WW; Bailer, AJ

HERO ID

5935443

Reference Type

Journal Article

Year

2019

Language

English

PMID

30368842

HERO ID 5935443
In Press No
Year 2019
Title 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
Authors Wheeler, MW; Piegorsch, WW; Bailer, AJ
Journal Risk Analysis
Volume 39
Issue 3
Page Numbers 616-629
Abstract Quantitative 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.
Doi 10.1111/risa.13218
Pmid 30368842
Url https://www.ncbi.nlm.nih.gov/pubmed/30368842
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English
Keyword Animals; Bayes Theorem; Databases, Factual; Dose-Response Relationship, Drug; Probability; Programming Languages; Public Health; Rats, Sprague-Dawley; Risk Assessment/methods; Software; Uncertainty; alpha-Chlorohydrin/analysis/toxicity; BMDS software; Bayesian prior elicitation; R software; carcinogenicity; data mining; knowledge base; quantal dose-response data; statistical methods; toxicology