Low dose risk estimation via simultaneous statistical inferences

Walter W Piegorsch, R. Webster West, Wei Pan, Ralph L. Kodell

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.

Original languageEnglish (US)
Pages (from-to)245-258
Number of pages14
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume54
Issue number1
DOIs
StatePublished - 2005
Externally publishedYes

Fingerprint

Simultaneous Inference
Statistical Inference
Dose
Confidence Bounds
Benchmark Dose
Confidence Limits
Risk Analysis
End point
Risk Assessment
Quantitative Analysis
Animals
Statistical inference
Estimation risk
Confidence

Keywords

  • Bench-mark dose
  • Environmental risk assessment
  • Non-quantal dose-response
  • Simultaneous confidence bands
  • Simultaneous inference

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Low dose risk estimation via simultaneous statistical inferences. / Piegorsch, Walter W; West, R. Webster; Pan, Wei; Kodell, Ralph L.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 54, No. 1, 2005, p. 245-258.

Research output: Contribution to journalArticle

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