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

Morales, KH; Ibrahim, JG; Chen, CJ; Ryan, LM

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

Morales, KH Ibrahim, JG Chen, CJ Ryan, LM

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Publication Type:: Journal Article
Citation:: Journal of the American Statistical Association, 2006, 101 (473), pp. 9 - 17
Issue Date:: 2006-03-01

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Field	Value	Language
dc.contributor.author	Morales, KH	en_US
dc.contributor.author	Ibrahim, JG	en_US
dc.contributor.author	Chen, CJ	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	2006-03-01	en_US
dc.identifier.citation	Journal of the American Statistical Association, 2006, 101 (473), pp. 9 - 17	en_US
dc.identifier.issn	0162-1459	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26009
dc.description.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 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. © 2006 American Statistical Association.	en_US
dc.relation.ispartof	Journal of the American Statistical Association	en_US
dc.relation.isbasedon	10.1198/016214505000000961	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Bayesian model averaging with applications to benchmark dose estimation for arsenic in drinking water	en_US
dc.type	Journal Article
utslib.citation.volume	473	en_US
utslib.citation.volume	101	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1603 Demography	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	473	en_US
pubs.publication-status	Published	en_US
pubs.volume	101	en_US

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 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. © 2006 American Statistical Association.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26009