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A Distributional Approach to Characterizing Low‐Dose Cancer Risk

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  • John S. Evans
  • John D. Graham
  • George M. Gray
  • Robert L. Sielken

Abstract

Since cancer risk at very low doses cannot be directly measured in humans or animals, mathematical extrapolation models and scientific judgment are required. This article demonstrates a probabilistic approach to carcinogen risk assessment that employs probability trees, subjective probabilities, and standard bootstrapping procedures. The probabilistic approach is applied to the carcinogenic risk of formaldehyde in environmental and occupational settings. Sensitivity analyses illustrate conditional estimates of risk for each path in the probability tree. Fundamental mechanistic uncertainties are characterized. A strength of the analysis is the explicit treatment of alternative beliefs about pharmacokinetics and pharmacodynamics. The resulting probability distributions on cancer risk are compared with the point estimates reported by federal agencies. Limitations of the approach are discussed as well as future research directions.

Suggested Citation

  • John S. Evans & John D. Graham & George M. Gray & Robert L. Sielken, 1994. "A Distributional Approach to Characterizing Low‐Dose Cancer Risk," Risk Analysis, John Wiley & Sons, vol. 14(1), pages 25-34, February.
    • Handle: RePEc:wly:riskan:v:14:y:1994:i:1:p:25-34
    DOI: 10.1111/j.1539-6924.1994.tb00025.x
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    References listed on IDEAS

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    Cited by:

    1. Adam M. Finkel & Benjamin D. Trump & Diana Bowman & Andrew Maynard, 2018. "A “solution-focused” comparative risk assessment of conventional and synthetic biology approaches to control mosquitoes carrying the dengue fever virus," Environment Systems and Decisions, Springer, vol. 38(2), pages 177-197, June.
    2. David M. Hassenzahl, 2006. "Implications of Excessive Precision for Risk Comparisons: Lessons from the Past Four Decades," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 265-276, February.
    3. Adam M. Finkel, 1994. "Stepping Out of Your Own Shadow: A Didactic Example of How Facing Uncertainty Can Improve Decision‐Making," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 751-761, October.

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