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A Comparison of Methods of Benchmark‐Dose Estimation for Continuous Response Data

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  • R. Webster West
  • Ralph L. Kodell

Abstract

Methods of quantitative risk assessment for toxic responses that are measured on a continuous scale are not well established. Although risk‐assessment procedures that attempt to utilize the quantitative information in such data have been proposed, there is no general agreement that these procedures are appreciably more efficient than common quantal dose‐response procedures that operate on dichotomized continuous data. This paper points out an equivalence between the dose‐response models of the nonquantal approach of Kodell and West(1)) and a quantal probit procedure, and provides results from a Monte Carlo simulation study to compare coverage probabilities of statistical lower confidence limits on dose corresponding to specified additional risk based on applying the two procedures to continuous data from a dose‐response experiment. The nonquantal approach is shown to be superior, in terms of both statistical validity and statistical efficiency.

Suggested Citation

  • R. Webster West & Ralph L. Kodell, 1999. "A Comparison of Methods of Benchmark‐Dose Estimation for Continuous Response Data," Risk Analysis, John Wiley & Sons, vol. 19(3), pages 453-459, June.
    • Handle: RePEc:wly:riskan:v:19:y:1999:i:3:p:453-459
    DOI: 10.1111/j.1539-6924.1999.tb00420.x
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    References listed on IDEAS

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    1. Ronald J. Bosch & David Wypij & Louise M. Ryan, 1996. "A Semiparametric Approach to Risk Assessment for Quantitative Outcomes," Risk Analysis, John Wiley & Sons, vol. 16(5), pages 657-665, October.
    2. D. Krewski & Y. Zhu, 1995. "A Simple Data Transformation for Estimating Benchmark Doses in Developmental Toxicity Experiments," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 29-39, February.
    3. Ralph L. Kodell & Ronnie W. West, 1993. "Upper Confidence Limits on Excess Risk for Quantitative Responses," Risk Analysis, John Wiley & Sons, vol. 13(2), pages 177-182, April.
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    Cited by:

    1. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    2. Miwako Dakeishi & Katsuyuki Murata & Akiko Tamura & Toyoto Iwata, 2006. "Relation Between Benchmark Dose and No‐Observed‐Adverse‐Effect Level in Clinical Research: Effects of Daily Alcohol Intake on Blood Pressure in Japanese Salesmen," Risk Analysis, John Wiley & Sons, vol. 26(1), pages 115-123, February.
    3. Salomon J. Sand & Dietrich Von Rosen & Agneta Falk Filipsson, 2003. "Benchmark Calculations in Risk Assessment Using Continuous Dose‐Response Information: The Influence of Variance and the Determination of a Cut‐Off Value," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1059-1068, October.
    4. Yasushi Suwazono & Kouichi Sakata & Mitsuhiro Oishi & Yasushi Okubo & Mirei Dochi & Etsuko Kobayashi & Teruhiko Kido & Koji Nogawa, 2007. "Estimation of Benchmark Dose as the Threshold Amount of Alcohol Consumption for Blood Pressure in Japanese Workers," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1487-1495, December.
    5. Matteo Goldoni & Maria Vittoria Vettori & Rossella Alinovi & Andrea Caglieri & Sandra Ceccatelli & Antonio Mutti, 2003. "Models of Neurotoxicity: Extrapolation of Benchmark Doses in Vitro," Risk Analysis, John Wiley & Sons, vol. 23(3), pages 505-514, June.

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