The impact of model uncertainty on benchmark dose estimation

R. Webster West, Walter W. Piegorsch, Edsel A. Peña, Lingling An, Wensong Wu, Alissa A. Wickens, Hui Xiong, Wenhai Chen

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)706-716
Number of pages11
Issue number8
StatePublished - Dec 2012


  • AIC
  • BMDL
  • Benchmark analysis
  • Excess risk
  • Extra risk
  • Model adequacy
  • Model selection
  • Quantitative risk assessment

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecological Modeling


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