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Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty

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  • Matthew W. Wheeler
  • Todd Blessinger
  • Kan Shao
  • Bruce C. Allen
  • Louis Olszyk
  • J. Allen Davis
  • Jeffrey S Gift

Abstract

Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.

Suggested Citation

  • Matthew W. Wheeler & Todd Blessinger & Kan Shao & Bruce C. Allen & Louis Olszyk & J. Allen Davis & Jeffrey S Gift, 2020. "Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1706-1722, September.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:9:p:1706-1722
    DOI: 10.1111/risa.13537
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    References listed on IDEAS

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    1. A. John Bailer & Robert B. Noble & Matthew W. Wheeler, 2005. "Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 291-299, April.
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    5. Matthew W. Wheeler & Walter W. Piegorsch & Albert John Bailer, 2019. "Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 616-629, March.
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