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# Re: st: Odds ratio

 From "E. Paul Wileyto" To statalist@hsphsun2.harvard.edu Subject Re: st: Odds ratio Date Thu, 08 Apr 2010 15:17:14 -0400

The problem is that vastly different sets of numbers can give you the same odds ratio... same odds ratio, with different variances. Effect size (in Cohenesque d speak) is best obtained from fractions and and changes in fractions. Your effect size can come from the log of the odds-ratio, but the variance will be determined by the actual proportions involved in calculating the OR.
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It doesn't sound like the reviewer is asking for much. Would it hurt to give the proportions?
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You can actually generate those effect size numbers (d) if you report an Odds Ratio with CI95 and a sample size, but that is more convoluted.
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P

Rosie Chen wrote:
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```Hello, dear all,

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I have a question regarding a reviewer's comment on my use of odds ratio in interpreting the results of a logistic regression, and would appreciate it very much if you can provide any insight or any references for responding to the comment. The reviewer commented that all results are expressed in terms of odds ratios which makes it very difficult to assess the magnitude of the effect. Probabilities and changes in probabilities would be much easier to interpret. My impression is that, although it is true that predicted probabilities might be easier to understand, odds ratios have been used extensively in research when we interpret results from logit models. Do you have any suggestions regarding how to respond to this comment, or do you have any statistics textbooks in your mind that recommend odds ratio as a standard approach reporting results from logistic models?
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Thank you very much in advance!

Rosie

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--
E. Paul Wileyto, Ph.D.
Assistant Professor of Biostatistics
Tobacco Use Research Center
School of Medicine, U. of Pennsylvania
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