Comparing model averaging with other model selection strategies for benchmark dose estimation |
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Authors: | Matthew W Wheeler A John Bailer |
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Institution: | (1) Risk Evaluation Branch, National Institute for Occupational Safety and Health, MS C-15, 4676 Columbia Parkway, Cincinnati, OH 45226, USA;(2) Center for Environmental Toxicology and Statistics, Department of Mathematics and Statistics, Miami University, Oxford, OH 45056, USA |
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Abstract: | Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use
of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the
use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various
assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit
to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates
from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to
the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method
has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further,
when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate
provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides
an alternative method for risk managers to estimate risk while incorporating model uncertainty.
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Keywords: | Bayesian model averaging Model uncertainty Risk estimation |
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