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The Use of Generalized Estimating Equations for Risk Assessment in Developmental Toxicity

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  • Louise Ryan

Abstract

This paper reviews and compares several approaches to fitting dose‐response models to developmental toxicity data. The main issue of interest is how to appropriately account for litter effects. Among the approaches reviewed are Beta Binomial models, models that attempt to characterize the litter effect through the use of covariates, and models that avoid the complication of correlated offspring by modeling “affected litter” rather than fetus‐specific outcomes. Finally, we discuss our recommended approach, which is to use Generalized Estimating Equations, or quasi‐likelihood. We give a number of reasons for preferring the latter and illustrate its application with an example.

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  • Louise Ryan, 1992. "The Use of Generalized Estimating Equations for Risk Assessment in Developmental Toxicity," Risk Analysis, John Wiley & Sons, vol. 12(3), pages 439-447, September.
    • Handle: RePEc:wly:riskan:v:12:y:1992:i:3:p:439-447
    DOI: 10.1111/j.1539-6924.1992.tb00696.x
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    Cited by:

    1. Daniel Krewski & Robert Smythe & Karen Y. Fung, 2002. "Optimal Designs for Estimating the Effective Dose in Developmental Toxicity Experiments," Risk Analysis, John Wiley & Sons, vol. 22(6), pages 1195-1205, December.
    2. Daniel O. Scharfstein & Paige L. Williams, 1994. "Design of Developmental Toxicity Studies for Assessing Joint Effects of Dose and Duration," Risk Analysis, John Wiley & Sons, vol. 14(6), pages 1057-1071, December.
    3. Paul Catalano & Louise Ryan & Daniel Scharfstein, 1994. "Modeling Fetal Death and Malformation in Developmental Toxicity Studies," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 629-637, August.
    4. D. Krewski & Y. Zhu, 1994. "Applications of Multinomial Dose‐Response Models in Developmental Toxicity Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 613-627, August.
    5. Martha Contreras & Louise M. Ryan, 2000. "Fitting Nonlinear and Constrained Generalized Estimating Equations with Optimization Software," Biometrics, The International Biometric Society, vol. 56(4), pages 1268-1271, December.
    6. Terra L. Slaton & Walter W. Piegorsch & Stephen D. Durham, 2000. "Estimation and Testing with Overdispersed Proportions Using the Beta-Logistic Regression Model of Heckman and Willis," Biometrics, The International Biometric Society, vol. 56(1), pages 125-133, March.

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