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From |
"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: Poisson Regression |

Date |
Tue, 15 Feb 2011 08:44:32 -0500 |

Very good point. But you also moved the discussion from ratio measures to difference measures (excess risk). And this is the essence of the discussion. There are subtleties of these measures that are not discussed in clinical articles, particularly when the measure -- like the odds ratio -- is not intuitive (even if it's only superficial). If the measure is not understood, the focus is on significance testing (which is also poorly understood in the clinical world). So the focus is not where it should be -- the clinical relevance. IMO, more frequent use of log-binomial and Poisson approaches in these specific types of clinical studies (common outcomes) can only help to refocus the discussion to the content that is relevant for the clinician. Regards, -p ________________________________________________ Paul F. Visintainer, PhD Baystate Medical Center Springfield, MA 01199 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Brendan Halpin Sent: Tuesday, February 15, 2011 5:41 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: RE: Poisson Regression On Mon, Feb 14 2011, Visintainer, Paul wrote: > My frustration is that when the outcome is common and logistic > regression is used, there virtually no discussion of clinical relevance > -- only statistical significance, (e.g., is a significant odds ratio of > 2.5 clinically relevant? Perhaps if the base risk is 2%; perhaps not if > the base risk 73%. Your underlying point about substantive significance is well taken, but this is a bad example. Here is a simulation (code below): Case 1: | outcome class | No Yes | Total -----------+----------------------+---------- Controls | 980 20 | 1,000 Treatment | 951 49 | 1,000 -----------+----------------------+---------- Total | 1,931 69 | 2,000 Case 1: RR 2.450 OR 2.525 N extra outcomes 29 Case 2: | outcome class | No Yes | Total -----------+----------------------+---------- Controls | 270 730 | 1,000 Treatment | 129 871 | 1,000 -----------+----------------------+---------- Total | 399 1,601 | 2,000 RR 1.193 OR 2.497 N extra outcomes 141 In both cases the OR is 2.5, and the rate for controls is respectively 2% and 73%. The RR is much lower with the 73% base rate. However, the "clinical" significance is *higher* with the 73% base rate, with 14.1% excess "outcomes" in the treatment group compared with 2.9% when the base rate is 2%. In other words, the relative rate seems a poorer, not a better estimate of clinical significance than the odds ratio. (In fact, a probit model looks even better with a 2% effect of 0.40 and a 73% effect of 0.52.) Brendan --8<----- clear input class outcome n 0 0 980 0 1 20 1 0 951 1 1 49 end label define class 0 "Controls" 1 "Treatment" label define outcome 0 "No" 1 "Yes" label values class class label values outcome outcome noi tab class outcome [freq=n], matcell(t) scalar relrate = (t[2,2]/(t[2,1]+t[2,2]))/(t[1,2]/(t[1,1]+t[1,2])) scalar OR = (t[2,2]/(t[2,1] ))/(t[1,2]/(t[1,1] )) scalar D = t[2,2] - t[1,2] noi di "Case 1:" _newline "RR " %6.3f relrate _newline "OR " %6.3f OR _newline "N extra outcomes" %5.0f D expand n noi probit outcome class clear input class outcome n 0 0 270 0 1 730 1 0 129 1 1 871 end label define class 0 "Controls" 1 "Treatment" label define outcome 0 "No" 1 "Yes" label values class class label values outcome outcome noi tab class outcome [freq=n], matcell(t) scalar relrate = (t[2,2]/(t[2,1]+t[2,2]))/(t[1,2]/(t[1,1]+t[1,2])) scalar OR = (t[2,2]/(t[2,1] ))/(t[1,2]/(t[1,1] )) scalar D = t[2,2] - t[1,2] noi di "Case 2:" _newline "RR " %6.3f relrate _newline "OR " %6.3f OR _newline "N extra outcomes" %5.0f D expand n noi probit outcome class --8<----- -- Brendan Halpin, Department of Sociology, University of Limerick, Ireland Tel: w +353-61-213147 f +353-61-202569 h +353-61-338562; Room F1-009 x 3147 mailto:brendan.halpin@ul.ie http://www.ul.ie/sociology/brendan.halpin.html * * 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/ ---------------------------------------------------------------------- Please view our annual report at http://baystatehealth.org/annualreport CONFIDENTIALITY NOTICE: This e-mail communication and any attachments may contain confidential and privileged information for the use of the designated recipients named above. If you are not the intended recipient, you are hereby notified that you have received this communication in error and that any review, disclosure, dissemination, distribution or copying of it or its contents is prohibited. If you have received this communication in error, please reply to the sender immediately or by telephone at 413-794-0000 and destroy all copies of this communication and any attachments. For further information regarding Baystate Health's privacy policy, please visit our Internet site at http://baystatehealth.org. * * 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: Poisson Regression***From:*Alexandra Boing <alexandraboing@yahoo.com.br>

**st: RE: Poisson Regression***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

**Re: st: RE: Poisson Regression***From:*brendan.halpin@ul.ie (Brendan Halpin)

**RE: st: RE: Poisson Regression***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

**Re: st: RE: Poisson Regression***From:*brendan.halpin@ul.ie (Brendan Halpin)

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