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Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water. (English) Zbl 1118.62373
Summary: 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 propose 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.

MSC:
62P10 Applications of statistics to biology and medical sciences; meta analysis
62P12 Applications of statistics to environmental and related topics
62F15 Bayesian inference
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