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# Re: st: RE: Odds ratio

 From "Michael I. Lichter" To statalist@hsphsun2.harvard.edu Subject Re: st: RE: Odds ratio Date Fri, 09 Apr 2010 14:09:53 -0400

Joseph (and anybody else),

A related question recently came up here. An applied statistician told a colleague that in her logistic regression analysis with a disease (yes/no) outcome, the risk-probability interpretation for odds ratios was not appropriate and that therefore she should use negative binomial regression with the same dichotomous outcome variable. He said that the anti-logged coefficients would be interpretable as risk ratios in this instance. Does that sound like a valid approach?

Michael

jhilbe@aol.com wrote:
I agree with Rich regarding the bias that can be produced in using oddsrisk. There are some situations for which it is acceptable, but mostly it is not. I wrote the command to provide it for those who wanted to use it - not to advocate its
use.  The method was proposed in an article by Zhang and Yu in 1998 in
JAMA -- Journal of the American Medical Association, presumably one of the
most elite of medical journals. So it gained considerable popularity.

I discuss the method in some length in my book "Logistic Regression Models", pointing out its problems,but also when it can be used. I also offer general guidelines of when it is acceptable to use a risk-probability interpretation for odds ratios, and
provide the calculations required to make the determination.

Joseph Hilbe

Date: Thu, 08 Apr 2010 16:21:49 -0400
From: Richard Goldstein <richgold@ix.netcom.com>
Subject: Re: st: RE: Odds ratio

well, first, I disagree with Paul's recommendation of -oddsrisk- there
is now lots of evidence that this algorithm is biased; for results, and
other ways of translating OR to RR see Blizzard, L and Hosmer, DW
(2006), "Parameter estimation and goodness-of-fit in log binomial
regression," _Biometrical Journal_, 48: 5-22; for a different way of
looking at this, with Stata code, see, Localio, AR, Margolis, DJ and
Berlin, JA (2007), "Relative risks and confidence intervals were easily
computed indirectly from multivariable logistic regression," _Journal of
Clinical Epidemiology_, 60: 874-882

second, if the OP really wants to present things in terms of changes in

Rich

On 4/8/10 4:05 PM, Visintainer, Paul wrote:
Rosie,

Many health professionals find the ORs difficult to interpret,
especially if
the base proportion is common.  It doesn't sound like the reviewer is
questioning your analysis, just the way you present the results -- and,
depending on your study, the ORs may look rather strange. For example, if you did a survey, and 60% of one group answered a question positively, while 90% of another group answered it positively, your OR would be 6.0. This can be quite
confusing for someone trying to interpret this OR like a relative risk.

One approach might be to try a program by Joseph Hilbe, called
-oddsrisk-.  It
will convert ORs from a logistic regression to a relative risk with 95%CIs. In the example above, the OR was 6, but the conversion gave 1.5 (e.g., .90/.60 =
1.5).  This probably will make more sense to certain readers.

Note that using the term "relative risk" depends on your study. If
your
logistic model was developed on a survey (e.g., cross-sectional), then your ORs are prevalence ORs. If you convert them using the -oddsrisk- program, you'll
have "prevalence ratios", not relative risks.

Best,

-p

________________________________________________
Paul F. Visintainer, PhD
Baystate Medical Center
Division of Academic Affairs - 3rd Floor
Springfield, MA 01199

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu]
On Behalf Of Rosie Chen
Sent: Thursday, April 08, 2010 2:48 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Odds ratio

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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--
Michael I. Lichter, Ph.D. <mlichter@buffalo.edu>
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 126 / Phone: 716-898-4751 / FAX: 716-898-3536

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