Benchmark Dose Analysis via Nonparametric Regression Modeling |
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Authors: | Walter W. Piegorsch Hui Xiong Rabi N. Bhattacharya Lizhen Lin |
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Affiliation: | 1. Program in Statistics, University of Arizona, , Tucson, AZ, USA;2. BIO5 Institute, University of Arizona, , Tucson, AZ, USA;3. Department of Mathematics, University of Arizona, , Tucson, AZ, USA;4. Program in Applied Mathematics, University of Arizona, , Tucson, AZ, USA;5. Department of Statistical Science, Duke University, , Durham, NC, USA |
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Abstract: | Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty. |
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Keywords: | Benchmark analysis BMD BMDL bootstrap confidence limits dose‐response analysis isotonic regression toxicological risk assessment |
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