Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses

A. John Bailer, Robert B. Noble and Matthew W. Wheeler

Risk Analysis, 2005, vol. 25, issue 2, 291-299

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.

Date: 2005
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Citations: View citations in EconPapers (21)

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https://doi.org/10.1111/j.1539-6924.2005.00590.x

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:25:y:2005:i:2:p:291-299

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