JSM 2004 - Toronto

Abstract #300661

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Activity Number: 81
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract - #300661
Title: Model Uncertainty and Risk Estimation for Quantal Responses
Author(s): A. John Bailer*+ and Robert Noble
Companies: Miami University and Miami University
Address: Dept. of Mathematics & Statistics/NIOSH, Oxford, OH, 45056,
Keywords: unit cancer risk ; Bayesian model averaging ; quantal multistage models
Abstract:

Observational human and 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 in the context of applying the quantal multistage model. An animal carcinogenicity study dataset is used to illustrate how BMA provides excess risk estimates that reflect both sampling variability and model uncertainty.

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