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From |
"Newson, Roger B" <[email protected]> |

To |
<[email protected]> |

Subject |
RE: st: Power calculation for Beta/Odds Ratios in logistic regression models |

Date |
Wed, 15 Aug 2007 21:47:52 +0100 |

I would argue that it depends what you mean by "post hoc power calculations". If Nico is proposing to calculate the power to detect a POPULATION odds ratio of the size of the observed SAMPLE odds ratio, then that would indeed be misleading and uninformative. The Central Limit ?Theorem implies that the power to detect a POPULATION odds ratio the size of the observed SAMPLE odds ratio, using the observed SAMPLE P-value as the threshold, will always be 0.5 (or 50 percent). Therefore, it will not be surprising if the study was underpowered to detect a POPULATION odds ratio as large as the observed SAMPLE odds ratio, even though the study may have been adequately powered to detect a larger POPULATION odds ratio, which was considered to be clinically interesting by the study designers at the time of the study design. HOWEVER, if Nico intends to use an existing study to inform the design of a possible larger (and therefore more sensitive) study, using the existing study as a pilot, then Nico is doing nothing wrong. In fact, Nico is probably doing the best that anybody can do, given that the proposed analysis method appears to be confounder-adjusted, implying that doing power calculations from first principles will be very complicated. And there may be a genuine lack of consensus regarding what effect size is "clinically interesting". I hope this helps. Best wishes Roger Roger Newson Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: [email protected] Web page: www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/pop genetics/reph/ Opinions expressed are those of the author, not of the institution. -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of uri goldbourt Sent: 15 August 2007 20:30 To: [email protected] Subject: RE: st: Power calculation for Beta/Odds Ratios in logistic regression models Well and importantly said! UG --------------------- -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Neil Shephard Sent: Wednesday, August 15, 2007 9:08 AM To: [email protected] Subject: Re: st: Power calculation for Beta/Odds Ratios in logistic regression models On 8/15/07, Nico Hutter <[email protected]> wrote: > Hi everyone, > > we would like to run a power calculation for beta-coefficients / odds > ratios in logistic regression models with covariates. > Is such a procedure implemented in SATA? > > Used command for the logistic regression model: > svy: logit depression age sex comorbidity, or > > Data stems from a national representative survey. Dependent variable is > depression. Comorbidity is dichotomised. Now, we are interested in the > "post hoc" power of the odds ratio of comorbidity. > > Can anybody give us some advice, please? Thanks in advance! Roger Newson has pointed to an appropriate solution, but I would question why you wish to do this? In my opinion "post hoc" power is a meaningless measurement that is completely useless in interpreting results, and a practice that needs to discouraged. You have already done your test (logistic regression) and got your results. Knowing the 'power' your data had of detecting this size of effect (or more likely lack of) will tell you nothing more informative about the association. Its like trying to tell someone who's just one the lottery that they shouldn't buy lottery tickets because the chance of winning is so low, they're not going to care as they've already got their answer. There are a few papers around that discuss this in greater detail (I'm sure there are more). Neil References Goodman SN, Berlin JA (1994) The Use of Predicted Confidence Intervals when Planning Experiments and the Misuse of Power When Interpreting Results. Annals of Internal Medicine 121.3:200-206 Hoenig J.M., Heisey D.M. (2001) The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis. The American Statistician 55:19-24 Levine M, Ensom MH (2001) Post hoc power analysis: an idea whose time has passed? Pharmacotherapy 21.4:405-409 -- "In mathematics you don't understand things. You just get used to them." - Johann von Neumann Email - [email protected] / [email protected] Website - http://slack.ser.man.ac.uk/ Photos - http://www.flickr.com/photos/slackline/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:

**References**:**Re: st: Power calculation for Beta/Odds Ratios in logistic regression models***From:*"Neil Shephard" <[email protected]>

**RE: st: Power calculation for Beta/Odds Ratios in logistic regressionmodels***From:*uri goldbourt <[email protected]>

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