title: Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment.
abstract: An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating benchmark doses, based on information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits' small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents.
authors.affiliation: Interdisciplinary Program in Statistics, University of Arizona, Tucson, AZ, USA.
authors.forename: Walter W
authors.initials: WW
authors.lastname: Piegorsch
authors.forename: Lingling
authors.initials: L
authors.lastname: An
authors.forename: Alissa A
authors.initials: AA
authors.lastname: Wickens
authors.forename: R Webster
authors.initials: RW
authors.lastname: West
authors.forename: Edsel A
authors.initials: EA
authors.lastname: Peña
authors.forename: Wensong
authors.initials: W
authors.lastname: Wu
keywords: Akaike information criterion (AIC); dose-response modeling; frequentist model averaging; model uncertainty; multi-model inference
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