Benchmark dose analysis via nonparametric regression modeling

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<title>Benchmark dose analysis via nonparametric regression modeling</title>
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<abstract displayLabel="Summary">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.</abstract>
<note type="statement of responsibility">Walter W. Piegorsch...[et.al]</note>
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<title>Risk analysis : an international journal</title>
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<publisher>McLean, Virginia : Society for Risk Analysis, 1987-2015</publisher>
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<identifier type="issn">0272-4332</identifier>
<identifier type="local">MAP20077000345</identifier>
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<text>13/01/2014 Volumen 34 Número 1 - enero 2014 </text>
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