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Expert Scientific Judgment and Cancer Risk Assessment: A Pilot Study of Pharmacokinetic Data

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  • Neil C. Hawkins
  • John D. Graham

Abstract

When high‐dose tumor data are extrapolated to low doses, it is typically assumed that the dose of a carcinogen delivered to target cells is proportional to the dose administered to test animals, even at exposure levels below the experimental range. Since pharmacokinetic data are becoming available that in some cases question the validity of this assumption, risk assessors must decide whether to maintain the standard assumption. A pilot study of formaldehyde is reported that was undertaken to demonstrate how expert scientific judgment can help guide a controversial risk assessment where pharmacokinetic data are considered inconclusive. Eight experts on pharmacokinetic data were selected by a formal procedure, and each was interviewed personally using a structured interview protocol. The results suggest that expert scientific opinion is polarized in this case, a situation that risk assessors can respond to with a range of risk characterizations considered biologically plausible by the experts. Convergence of expert opinion is likely in this case if several specific research strategies are executed in a competent fashion. Elicitation of expert scientific judgment is a promising vehicle for evaluating the quality of pharmacokinetic data, expressing uncertainty in risk assessment, and fashioning a research agenda that offers possible forging of scientific consensus.

Suggested Citation

  • Neil C. Hawkins & John D. Graham, 1988. "Expert Scientific Judgment and Cancer Risk Assessment: A Pilot Study of Pharmacokinetic Data," Risk Analysis, John Wiley & Sons, vol. 8(4), pages 615-625, December.
    • Handle: RePEc:wly:riskan:v:8:y:1988:i:4:p:615-625
    DOI: 10.1111/j.1539-6924.1988.tb01205.x
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    References listed on IDEAS

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    1. Kenneth G. Brown, 1985. "Risk Assessment of Laboratory Rats and Mice Chronically Exposed to Formaldehyde Vapors," Risk Analysis, John Wiley & Sons, vol. 5(3), pages 171-180, September.
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    Cited by:

    1. Mitchell J. Small, 2008. "Methods for Assessing Uncertainty in Fundamental Assumptions and Associated Models for Cancer Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1289-1308, October.

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