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From |
brendan.halpin@ul.ie (Brendan Halpin) |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Poisson Regression |

Date |
Tue, 15 Feb 2011 10:40:53 +0000 |

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/

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

**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>

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