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The impact of model uncertainty on benchmark dose estimation
Author(s) -
West R. Webster,
Piegorsch Walter W.,
Peña Edsel A.,
An Lingling,
Wu Wensong,
Wickens Alissa A.,
Xiong Hui,
Chen Wenhai
Publication year - 2012
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.2180
Subject(s) - benchmark (surveying) , parametric statistics , selection (genetic algorithm) , econometrics , confidence interval , monte carlo method , model selection , estimation , parametric model , statistics , computer science , mathematics , economics , machine learning , management , geodesy , geography
We study the popular benchmark dose (BMD) approach for estimation of low exposure levels in toxicological risk assessment, focusing on dose–response experiments with quantal data. In such settings, representations of the risk are traditionally based on a specified, parametric, dose–response model. It is a well‐known concern, however, that uncertainty can exist in specification and selection of the model. If the chosen parametric form is in fact misspecified, this can lead to inaccurate, and possibly unsafe, low‐dose inferences. We study the effects of model selection and possible misspecification on the BMD, on its corresponding lower confidence limit (BMDL), and on the associated extra risks achieved at these values, via large‐scale Monte Carlo simulation. It is seen that an uncomfortably high percentage of instances can occur where the true extra risk at the BMDL under a misspecified or incorrectly selected model can surpass the target benchmark response, exposing potential dangers of traditional strategies for model selection when calculating BMDs and BMDLs. Copyright © 2012 John Wiley & Sons, Ltd.