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Model uncertainty and risk estimation for experimental studies of quantal responses.
Bailer AJ; Noble RB; Wheeler MW
Risk Anal 2005 Apr; 25(2):291-299
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.
Models; Risk-analysis; Risk-factors; Occupational-hazards; Occupational-exposure; Exposure-levels; Mathematical-models; Kidneys; Cancer; Statistical-analysis; Author Keywords: Bayesian model averaging; benchmark doses; quantal multistage models; unit cancer risk
Risk Evaluation Branch, National Institute for Occupational Safety and Health, 4676 Columbia Parkway, Cincinnati, OH 45224, USA
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Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division