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Statistical issues on the no-observed-adverse-effect level in categorical response.

The determination of the value of the no-observed-adverse-effect level (NOAEL) when observed responses can be categorized by severity (categorical data) and sample sizes are small is discussed. The common situation of only two categories, where only the presence or absence of an effect is observed,...

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Detalles Bibliográficos
Autores principales: Yanagawa, T, Kikuchi, Y, Brown, K G
Formato: Texto
Lenguaje:English
Publicado: 1994
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566885/
https://www.ncbi.nlm.nih.gov/pubmed/8187733
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author Yanagawa, T
Kikuchi, Y
Brown, K G
author_facet Yanagawa, T
Kikuchi, Y
Brown, K G
author_sort Yanagawa, T
collection PubMed
description The determination of the value of the no-observed-adverse-effect level (NOAEL) when observed responses can be categorized by severity (categorical data) and sample sizes are small is discussed. The common situation of only two categories, where only the presence or absence of an effect is observed, is addressed first (dichotomous data). Three tests for dichotomous data are critically examined, including the Brown-La Vange test, a modified version of that test, and Dunnett's multiple comparison test. Although the modified test is an improvement, all three procedures have shortcomings in determining the value of the NOAEL, particularly when the sample size is small. An alternative method is suggested, based on the Akaike information criterion (AIC), which performs well. This method is extended to severity data with an arbitrary number of categories. Use of a dose-response curve for the NOAEL is discussed.
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spelling pubmed-15668852006-09-19 Statistical issues on the no-observed-adverse-effect level in categorical response. Yanagawa, T Kikuchi, Y Brown, K G Environ Health Perspect Research Article The determination of the value of the no-observed-adverse-effect level (NOAEL) when observed responses can be categorized by severity (categorical data) and sample sizes are small is discussed. The common situation of only two categories, where only the presence or absence of an effect is observed, is addressed first (dichotomous data). Three tests for dichotomous data are critically examined, including the Brown-La Vange test, a modified version of that test, and Dunnett's multiple comparison test. Although the modified test is an improvement, all three procedures have shortcomings in determining the value of the NOAEL, particularly when the sample size is small. An alternative method is suggested, based on the Akaike information criterion (AIC), which performs well. This method is extended to severity data with an arbitrary number of categories. Use of a dose-response curve for the NOAEL is discussed. 1994-01 /pmc/articles/PMC1566885/ /pubmed/8187733 Text en
spellingShingle Research Article
Yanagawa, T
Kikuchi, Y
Brown, K G
Statistical issues on the no-observed-adverse-effect level in categorical response.
title Statistical issues on the no-observed-adverse-effect level in categorical response.
title_full Statistical issues on the no-observed-adverse-effect level in categorical response.
title_fullStr Statistical issues on the no-observed-adverse-effect level in categorical response.
title_full_unstemmed Statistical issues on the no-observed-adverse-effect level in categorical response.
title_short Statistical issues on the no-observed-adverse-effect level in categorical response.
title_sort statistical issues on the no-observed-adverse-effect level in categorical response.
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566885/
https://www.ncbi.nlm.nih.gov/pubmed/8187733
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