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Computing Power and Sample Size for Informational Odds Ratio (†)

The informational odds ratio (IOR) measures the post-exposure odds divided by the pre-exposure odds (i.e., information gained after knowing exposure status). A desirable property of an adjusted ratio estimate is collapsibility, wherein the combined crude ratio will not change after adjusting for a v...

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Detalles Bibliográficos
Autor principal: Efird, Jimmy T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3823319/
https://www.ncbi.nlm.nih.gov/pubmed/24157518
http://dx.doi.org/10.3390/ijerph10105239
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author Efird, Jimmy T.
author_facet Efird, Jimmy T.
author_sort Efird, Jimmy T.
collection PubMed
description The informational odds ratio (IOR) measures the post-exposure odds divided by the pre-exposure odds (i.e., information gained after knowing exposure status). A desirable property of an adjusted ratio estimate is collapsibility, wherein the combined crude ratio will not change after adjusting for a variable that is not a confounder. Adjusted traditional odds ratios (TORs) are not collapsible. In contrast, Mantel-Haenszel adjusted IORs, analogous to relative risks (RRs) generally are collapsible. IORs are a useful measure of disease association in case-referent studies, especially when the disease is common in the exposed and/or unexposed groups. This paper outlines how to compute power and sample size in the simple case of unadjusted IORs.
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spelling pubmed-38233192013-11-11 Computing Power and Sample Size for Informational Odds Ratio (†) Efird, Jimmy T. Int J Environ Res Public Health Communication The informational odds ratio (IOR) measures the post-exposure odds divided by the pre-exposure odds (i.e., information gained after knowing exposure status). A desirable property of an adjusted ratio estimate is collapsibility, wherein the combined crude ratio will not change after adjusting for a variable that is not a confounder. Adjusted traditional odds ratios (TORs) are not collapsible. In contrast, Mantel-Haenszel adjusted IORs, analogous to relative risks (RRs) generally are collapsible. IORs are a useful measure of disease association in case-referent studies, especially when the disease is common in the exposed and/or unexposed groups. This paper outlines how to compute power and sample size in the simple case of unadjusted IORs. MDPI 2013-10-21 2013-10 /pmc/articles/PMC3823319/ /pubmed/24157518 http://dx.doi.org/10.3390/ijerph10105239 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Communication
Efird, Jimmy T.
Computing Power and Sample Size for Informational Odds Ratio (†)
title Computing Power and Sample Size for Informational Odds Ratio (†)
title_full Computing Power and Sample Size for Informational Odds Ratio (†)
title_fullStr Computing Power and Sample Size for Informational Odds Ratio (†)
title_full_unstemmed Computing Power and Sample Size for Informational Odds Ratio (†)
title_short Computing Power and Sample Size for Informational Odds Ratio (†)
title_sort computing power and sample size for informational odds ratio (†)
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3823319/
https://www.ncbi.nlm.nih.gov/pubmed/24157518
http://dx.doi.org/10.3390/ijerph10105239
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