Benchmark dose analysis via nonparametric regression modeling

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<dc:date>2014-01-13</dc:date>
<dc:description xml:lang="es">Sumario: 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.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/146116.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Benchmark dose analysis via nonparametric regression modeling</dc:title>
<dc:relation xml:lang="es">En: Risk analysis : an international journal. - McLean, Virginia : Society for Risk Analysis, 1987-2015 = ISSN 0272-4332. - 13/01/2014 Volumen 34 Número 1 - enero 2014 </dc:relation>
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