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From |
"Edgar Munoz" <munozedg@hotmail.com> |

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

Subject |
RE: st: power calculations |

Date |
Fri, 5 Feb 2010 11:15:55 -0600 |

Also this could be useful... QUANTO - Version 1.2 - January 2007 - Developed by: Jim Gauderman, Ph.D. and John Morrison, M.S. Department of Preventive Medicine University of Southern California e-mail: jimg@usc.edu, jmorr@usc.edu Updates and Information: http://hydra.usc.edu/gxe Thanks. Edgar -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Neil Shephard Sent: Friday, February 05, 2010 3:32 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: power calculations On Thu, Feb 4, 2010 at 8:38 PM, louise hornstrup <louisehornstrup@hotmail.com> wrote: > Dear listers, > > I am doing two diffent studies, using Stata 11. The first is a cross-sectional study (n=42,298), the second a case-control study (n1=4,851/n2=4,851). > > I am looking at a specific genotype with a frequency of 0.6% in both studies (variable of interest), the endpoint is IHD (Ischemic Heart Disease). I have some difficulties with the power calculations. I want to estimate the minimal value (odds ratio) that can be detected with 80% power for both studies...But what is the right way to do this in Stata? As far as I can tell the sampsi command can+determine power or sample size - not minimal detectable effect size? > Its not a Stata solution but you may also find the excellent Genetic Power Calculator by Shaun Purcell and colleagues of use... Purcell S, Cherny SS, Sham PC. (2003) Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics, 19(1):149-150. http://pngu.mgh.harvard.edu/~purcell/gpc/ You can of course script (in Perl, Python [your choice of scripting language]) repeated queries to the server for a range of parameters to give a better view of the power of your sample than a single point estimate. Although of course knowing the power your sample has to detect different effect sizes isn't particularly enlightening and is kind of back to front as you should estimate your effect size from published studies/pilot studies and recruit an appropriately sized cohort to detect the estimated effect. If you've already done your study and not found any association calculating the effect size your sample has sufficient power to detect is not a good idea. A couple of references on this are below, but there are more out there. 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 Neil -- "... no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas." - Sir Ronald A. Fisher (1956) Email - nshephard@gmail.com Website - http://slack.ser.man.ac.uk/ Photos - http://www.flickr.com/photos/slackline/ * * 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/ * * 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: power calculations***From:*louise hornstrup <louisehornstrup@hotmail.com>

**Re: st: power calculations***From:*Neil Shephard <nshephard@gmail.com>

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