  An important objective in biomedical risk assessment is estimation of minimum
exposure levels that induce a pre-specified adverse response in a target
population. The exposure/dose points in such settings are known as Benchmark
Doses (BMDs). Recently, parametric Bayesian estimation for finding BMDs has
become popular. A large variety of candidate dose-response models is available
for applying these methods, however, leading to questions of model adequacy and
uncertainty. Here we enhance the Bayesian estimation technique for BMD analysis
by applying Bayesian model averaging to produce point estimates and (lower)
credible bounds. We include reparameterizations of traditional dose-response
models that allow for more-focused use of elicited prior information when
building the Bayesian hierarchy. Performance of the method is evaluated via a
short simulation study. An example from carcinogenicity testing illustrates the
calculations.
