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Computing Power and Sample Size for Informational Odds Ratio†
Center for Health Disparities Research and Department of Public Health, Brody School of Medicine, Greenville, NC 27858, USA
† This paper is an extended version of paper presented at the Ninth International Symposium on Recent Advances in Environmental Health Research, Jackson, MS, USA; 16–19 September 2012.
Received: 16 November 2012; in revised form: 28 December 2012 / Accepted: 14 January 2013 / Published: 21 October 2013
Abstract: 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.
Keywords: informational odds ratios; power; sample size
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Cite This Article
MDPI and ACS Style
Efird, J.T. Computing Power and Sample Size for Informational Odds Ratio. Int. J. Environ. Res. Public Health 2013, 10, 5239-5243.
Efird JT. Computing Power and Sample Size for Informational Odds Ratio. International Journal of Environmental Research and Public Health. 2013; 10(10):5239-5243.
Efird, Jimmy T. 2013. "Computing Power and Sample Size for Informational Odds Ratio." Int. J. Environ. Res. Public Health 10, no. 10: 5239-5243.