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Model uncertainty and risk estimation for experimental studies of quantal responses.

Authors
Bailer-AJ; Noble-RB; Wheeler-MW
Source
Risk Anal 2005 Apr; 25(2):291-299
NIOSHTIC No.
20026845
Abstract
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.
Keywords
Models; Risk-analysis; Risk-factors; Occupational-hazards; Occupational-exposure; Exposure-levels; Mathematical-models; Kidneys; Cancer; Statistical-analysis
Contact
Risk Evaluation Branch, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Cincinnati, OH 45224, USA
CODEN
RIANDF
Publication Date
20050401
Document Type
Journal Article
Email Address
baileraj@muohio.edu
Fiscal Year
2005
NTIS Accession No.
NTIS Price
Issue of Publication
2
ISSN
0272-4332
NIOSH Division
EID
Source Name
Risk Analysis
State
OH
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