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| From | Austin Nichols <austinnichols@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: power repeated measures anova vs mixed models |
| Date | Thu, 24 May 2012 13:53:54 -0400 |
Ricardo Ovaldia <ovaldia@yahoo.com>: Did you look at page 6 of the link? Any of the references? On Thu, May 24, 2012 at 1:45 PM, Ricardo Ovaldia <ovaldia@yahoo.com> wrote: > > Thank you Austin for the insightful remark: "Of course what you plug in matters" > > You are missing the point. I have the sample size (n=65/group), power (80%) and alpha (5%), 3 groups and 6 time points. What I want to compute is the minimal detectable effect size. I did the power analysis using a repeated measure ANOVA and obtained the minimal detectable effect sizes assuming various correlations between the repeated measurements. What I want to know is whether the mixed model would have more power to detect these effect sizes? > > Ricardo Ovaldia, MS > Statistician > Oklahoma City, OK > > > --- On Thu, 5/24/12, Austin Nichols <austinnichols@gmail.com> wrote: > >> From: Austin Nichols <austinnichols@gmail.com> >> Subject: Re: st: power repeated measures anova vs mixed models >> To: statalist@hsphsun2.harvard.edu >> Date: Thursday, May 24, 2012, 11:59 AM >> Ricardo Ovaldia <ovaldia@yahoo.com>: >> Of course what you plug in matters--see also: >> http://www.urban.org/UploadedPDF/1001394-clustered-randomization.pdf >> (esp. page 6) and references therein. >> You need estimates for the relevant information before you >> can estimate power. >> >> On Thu, May 24, 2012 at 12:39 PM, Ricardo Ovaldia <ovaldia@yahoo.com> >> wrote: >> > Thank you David. >> > I played with this a few day ago. The problem is that >> you have to make a lot of assumptions that I do not feel >> comfortably making because I lack prior knowledge about >> parameters, covariances, etc. The program produces very >> different results depending on what you "plug in". >> > >> > Ricardo >> > >> > Ricardo Ovaldia, MS >> > Statistician >> > Oklahoma City, OK >> > >> > >> > --- On Thu, 5/24/12, Airey, David C <david.airey@vanderbilt.edu> >> wrote: >> > >> >> From: Airey, David C <david.airey@vanderbilt.edu> >> >> Subject: re: st: power repeated measures anova vs >> mixed models >> >> To: "statalist@hsphsun2.harvard.edu" >> <statalist@hsphsun2.harvard.edu> >> >> Date: Thursday, May 24, 2012, 10:01 AM >> >> . >> >> >> >> I just came across this software for longitudinal >> / >> >> hierarchical experimental design power analysis: >> >> >> >> http://sitemaker.umich.edu/group-based/optimal_design_software >> >> >> >> I've not used it, but it might help you avoid >> simulation. >> >> >> >> -Dave >> >> >> >> > Dear all, >> >> > >> >> > I have been struggling to find an answer or >> reference >> >> to this problem. >> >> > >> >> > I am planning a longitudinal analysis >> comparing 3 >> >> groups with 6 time points per subject. The design >> is balance >> >> with 65 subjects for group. >> >> > >> >> > Because I do not have preliminary data and do >> not want >> >> to make unrealistic assumptions about the >> covariate >> >> structure and other parameters required to >> calculate power >> >> for mixed models, I decided to use repeated >> measures ANOVA >> >> to estimate the minimum detectable effect size at >> 80% power. >> >> >> >> > My questions are, will the mixed model have >> more power >> >> that the repeated measures ANOVA in this case? Are >> there any >> >> references regarding these comparisons? >> >> > >> >> > Thank you, >> >> > Ricardo >> >> > >> >> > Ricardo Ovaldia, MS >> >> > Statistician >> >> > Oklahoma City, OK >> * * 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/