Benchmark Dose Analysis via Nonparametric Regression Modeling

Risk Analysis - Tập 34 Số 1 - Trang 135-151 - 2014
Walter W. Piegorsch1,2,3, Hui Xiong4, Rabi Bhattacharya2,3, Lizhen Lin5
1BIO5 Institute, University of Arizona, Tucson, AZ, USA
2Department of Mathematics, University of Arizona, Tucson, AZ, USA
3Program in Statistics, University of Arizona, Tucson, AZ, USA
4Program in Applied Mathematics, University of Arizona, Tucson, AZ, USA
5Department of Statistical Science, Duke University, Durham, NC, USA;

Tóm tắt

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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Risk Analysis

Tập 34 Số 1

135-151

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