Benchmark dose analysis via nonparametric regression modeling

Walter W. Piegorsch, Hui Xiong, Rabi N. Bhattacharya, Lizhen Lin

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits' small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.

Original languageEnglish (US)
Pages (from-to)135-151
Number of pages17
JournalRisk Analysis
Issue number1
StatePublished - Jan 2014


  • BMD
  • BMDL
  • Benchmark analysis
  • Bootstrap confidence limits
  • Dose-response analysis
  • Isotonic regression
  • Toxicological risk assessment

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)


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