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Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses
Author(s) -
Bailer A. John,
Noble Robert B.,
Wheeler Matthew W.
Publication year - 2005
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2005.00590.x
Subject(s) - benchmark (surveying) , statistics , econometrics , statistical model , bayesian probability , model selection , estimation , bayesian inference , computer science , mathematics , engineering , geodesy , systems engineering , geography
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.