The impact of model uncertainty on benchmark dose estimation
Citations Over TimeTop 10% of 2012 papers
Abstract
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, lowdose 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 BMR, exposing potential dangers of traditional strategies for model selection when calculating BMDs and BMDLs.
Related Papers
- → A comparison of parametric, semi-parametric, and non-parametric approaches to selectivity in age-structured assessment models(2013)22 cited
- → Utilizing semi-parametric model to compensate systematic errors in photogrammetry(2009)1 cited
- → A Non Parametric Model for Magneto Rheological Dampers(1999)9 cited
- Shape constraints from parametric and non-parametric models(1993)
- → Parametric Models for Analysis of Common Cause Failure(1990)