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Model Selection and Estimation with Quantal‐Response Data in Benchmark Risk Assessment |
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| Authors: | Wensong Wu Walter Piegorsch Ronald W. West LingLing An |
| Affiliation: | 1. Department of Mathematics and Statistics, Florida International University, Miami, FL, USAThese authors contributed equally in this article.;2. Program in Statistics and BIO5 Institute, University of Arizona, Tucson, AZ, USA;3. Department of Statistics, North Carolina State University, Raleigh, NC, USA;4. Program of Statistics, Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ, USA |
| Abstract: | This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data “double‐dipping” and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided. |
| Keywords: | Focused‐inference approach information measures model averaging model selection problem pooled adjacent violators algorithm (PAVA) quantal‐dose response two‐step estimation approach |