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Information‐theoretic model‐averaged benchmark dose analysis in environmental risk assessment
Author(s) -
Piegorsch Walter W.,
An Lingling,
Wickens Alissa A.,
Webster West R.,
Peña Edsel A.,
Wu Wensong
Publication year - 2013
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2201
Subject(s) - benchmark (surveying) , frequentist inference , parametric statistics , computer science , confidence interval , econometrics , risk assessment , parametric model , model selection , nonparametric statistics , statistics , risk analysis (engineering) , machine learning , mathematics , bayesian probability , bayesian inference , artificial intelligence , medicine , computer security , geodesy , geography
An important objective in environmental risk assessment is estimation of minimum exposure levels, called benchmark doses (BMDs), that induce a pre‐specified benchmark response 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 used 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 the development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating BMDs, on the basis of 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. Copyright © 2013 John Wiley & Sons, Ltd.