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Re: st: Odds ratio


From   Marcello Pagano <pagano@hsph.harvard.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Odds ratio
Date   Fri, 09 Apr 2010 13:38:42 -0400

Bookmakers have no problem interpreting odds.
Why people go through all sorts of contortions to avoid odds when they are as natural as probabilities is beyond me. If you take m/n as your probability then m/(n-m) are your odds. One is easier than the other? Very odd.

m.p.

Lachenbruch, Peter wrote:
I almost agree - ORs are tough to interpret for non-Epidemiologists and Statisticians.  However, logistic regression is designed to estimate log-odds ratios.  I'd report both so both of you can feel 'happy' - of course, if the difference in probability of event is tiny, a large OR doesn't really indicate a big issue

Tony

Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of E. Paul Wileyto
Sent: Thursday, April 08, 2010 12:17 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Odds ratio

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.

It doesn't sound like the reviewer is asking for much. Would it hurt to give the proportions?

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.

P


Rosie Chen wrote:
Hello, dear all,

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?

Thank you very much in advance!

Rosie



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