Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses
Public Domain
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2005/04/01
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Description:Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty. [Description provided by NIOSH]
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ISSN:0272-4332
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Pages in Document:291-299
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Volume:25
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Issue:2
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NIOSHTIC Number:nn:20026845
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Citation:Risk Anal 2005 Apr; 25(2):291-299
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Contact Point Address:Risk Evaluation Branch, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Cincinnati, OH 45224, USA
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Email:baileraj@muohio.edu
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Federal Fiscal Year:2005
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Peer Reviewed:True
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Source Full Name:Risk Analysis
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Main Document Checksum:urn:sha-512:6200d3f655ce888693677f5b50d0c5552f1fd11feac634ee6715ef5c01ae58d3c11399cca0d516844eef68d25e5331d9e6ddbc907e4951910fc4272442ffa660
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