Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 101, Issue 473, Pages 9-17Publisher
TAYLOR & FRANCIS INC
DOI: 10.1198/016214505000000961
Keywords
dose-response model; generalized linear model; model uncertainty; quantitative risk assessment
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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.
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