BAYESIAN MODEL AVERAGING WITH APPLICATIONS TO BENCHMARK DOSE ESTIMATION FOR ARSENIC IN DRINKING WATER
Resource
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION v.101 n.473 pp.9-17
Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Journal Volume
v.101
Journal Issue
n.473
Pages
9-17
Date Issued
2006
Date
2006
Author(s)
CHEN, CHIEN-JEN
Abstract
An important component of quantitative risk assessment involves characterizing the dose-response relationship between an environmental exposure and adverse health outcome and then computing a benchmark dose, or the exposure level that yields a suitably low risk. This task is often complicated by model choice considerations, because risk estimates depend on the model parameters. We pro pose using Bayesian methods to address the problem of model selection and derive a model-averaged version of the benchmark dose. We illustrate the methods through application to data on arsenic-induced lung cancer from Taiwan.
Subjects
dose-response model
generalized linear model
model uncertainty
quantitative risk assessment
SDGs
Type
journal article