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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Odds ratio |

Date |
Thu, 08 Apr 2010 16:21:49 -0400 |

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 probability, I recommend looking at Scott Long's -spostado- (-findit spostado-) 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Odds ratio***From:*Rosie Chen <jiarongchen2002@yahoo.com>

**st: RE: Odds ratio***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

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