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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2013
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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. |
format | Online Article Text |
id | pubmed-3823319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT efirdjimmyt computingpowerandsamplesizeforinformationaloddsratio |