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Benchmark Dose Analysis via Nonparametric Regression Modeling
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DOI：10.1111/risa.12066.png)
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Abstract
En 中文
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.
Keywords:
Benchmark analysis
BMD
BMDL
bootstrap confidence limits
dose-response analysis
isotonic regression
toxicological risk assessment
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