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Dose‐Response Modeling with Summary Data from Developmental Toxicity Studies

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  • John F. Fox
  • Karen A. Hogan
  • Allen Davis

Abstract

Dose‐response analysis of binary developmental data (e.g., implant loss, fetal abnormalities) is best done using individual fetus data (identified to litter) or litter‐specific statistics such as number of offspring per litter and proportion abnormal. However, such data are not often available to risk assessors. Scientific articles usually present only dose‐group summaries for the number or average proportion abnormal and the total number of fetuses. Without litter‐specific data, it is not possible to estimate variances correctly (often characterized as a problem of overdispersion, intralitter correlation, or “litter effect”). However, it is possible to use group summary data when the design effect has been estimated for each dose group. Previous studies have demonstrated useful dose‐response and trend test analyses based on design effect estimates using litter‐specific data from the same study. This simplifies the analysis but does not help when litter‐specific data are unavailable. In the present study, we show that summary data on fetal malformations can be adjusted satisfactorily using estimates of the design effect based on historical data. When adjusted data are then analyzed with models designed for binomial responses, the resulting benchmark doses are similar to those obtained from analyzing litter‐level data with nested dichotomous models.

Suggested Citation

  • John F. Fox & Karen A. Hogan & Allen Davis, 2017. "Dose‐Response Modeling with Summary Data from Developmental Toxicity Studies," Risk Analysis, John Wiley & Sons, vol. 37(5), pages 905-917, May.
    • Handle: RePEc:wly:riskan:v:37:y:2017:i:5:p:905-917
    DOI: 10.1111/risa.12667
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    References listed on IDEAS

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    1. 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.
    2. Karen Y. Fung & Leonora Marro & Daniel Krewski, 1998. "A Comparison of Methods for Estimating the Benchmark Dose Based on Overdispersed Data from Developmental Toxicity Studies," Risk Analysis, John Wiley & Sons, vol. 18(3), pages 329-342, June.
    3. K. Y. Fung & D. Krewski & J. N. K. Rao & A. J. Scott, 1994. "Tests for Trend in Developmental Toxicity Experiments with Correlated Binary Data," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 639-648, August.
    4. Julie S. Najita & Paul J. Catalano, 2013. "On Determining the BMD from Multiple Outcomes in Developmental Toxicity Studies when One Outcome is Intentionally Missing," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1500-1509, August.
    5. Lanjia Lin & Dipankar Bandyopadhyay & Stuart R. Lipsitz & Debajyoti Sinha, 2010. "Association Models for Clustered Data with Binary and Continuous Responses," Biometrics, The International Biometric Society, vol. 66(1), pages 287-293, March.
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    Cited by:

    1. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.

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