Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water

• 成果类型: Article
• 署名作者: Morales, KH; Ibrahim, JG; Chen, CJ; Ryan, LM
• 署名单位: University of Pennsylvania; University of Pennsylvania; University of North Carolina; University of North Carolina Chapel Hill; National Taiwan University; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000961
• 发表日期: 2006
• 页码: 9-17
• 关键词: cancer mortality; endemic area; well water; RISK; uncertainty; selection; bladder; lung

摘要

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.