Multiplicity-adjusted inferences in risk assessment: Benchmark analysis with quantal response data

Daniela K. Nitcheva, Walter W. Piegorsch, R. Webster West, Ralph L. Kodell

Research output: Contribution to journalReview article

20 Scopus citations

Abstract

A primary objective in quantitative risk or safety assessment is characterization of the severity and likelihood of an adverse effect caused by a chemical toxin or pharmaceutical agent. In many cases data are not available at low doses or low exposures to the agent, and inferences at those doses must be based on the high-dose data. A modern method for making low-dose inferences is known as benchmark analysis, where attention centers on the dose at which a fixed benchmark level of risk is achieved. Both upper confidence limits on the risk and lower confidence limits on the "benchmark dose" are of interest. In practice, a number of possible benchmark risks may be under study; if so, corrections must be applied to adjust the limits for multiplicity. In this short note, we discuss approaches for doing so with quantal response data.

Original languageEnglish (US)
Pages (from-to)277-286
Number of pages10
JournalBiometrics
Volume61
Issue number1
DOIs
StatePublished - Mar 1 2005
Externally publishedYes

Keywords

  • Benchmark dose
  • Low-dose extrapolation
  • Multistage model
  • Quantal data
  • Quantitative risk assessment
  • Safety assessment
  • Simultaneous inferences

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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