Logit mixed model power analysis
I understand post-hoc power as being a function of the p-value, and therefore redundant. When asked for the kind of information that post-hoc power is used to represent, I prefer to provide confidence intervals on the parameters of interest - if possible. Best wishes, Andrew On Wed, Sep 16, 2015 at 11:29 AM, David Duffy <
David.Duffy at qimrberghofer.edu.au> wrote:
On Wed, 16 Sep 2015, Paul Johnson wrote: I second what Thierry said about the pointlessness of post-hoc power
analysis when using the observed effect size.
Post-hoc power tests are not very informative. You will get a high power when the signal is significant and low power when not significant.
A few people defend it as an estimate of the reproducibility probability. Gelman and Carlin suggest plugging in a "sensible" (Bayesian) estimate of the true effect based on your prior knowledge, so you can estimate the probability of a "Type S" (sign of true effect could actually be opposite to your study point estimate) or "Type M" error. Cheers, David. | David Duffy (MBBS PhD) | email: David.Duffy at qimrberghofer.edu.au ph: INT+61+7+3362-0217 fax: -0101 | Genetic Epidemiology, QIMR Berghofer Institute of Medical Research | 300 Herston Rd, Brisbane, Queensland 4006, Australia GPG 4D0B994A
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Andrew Robinson Deputy Director, CEBRA, School of Biosciences Reader & Associate Professor in Applied Statistics Tel: (+61) 0403 138 955 School of Mathematics and Statistics Fax: +61-3-8344 4599 University of Melbourne, VIC 3010 Australia Email: a.robinson at ms.unimelb.edu.au Website: http://www.ms.unimelb.edu.au/~andrewpr MSME: http://www.crcpress.com/product/isbn/9781439858028 FAwR: http://www.ms.unimelb.edu.au/~andrewpr/FAwR/ SPuR: http://www.ms.unimelb.edu.au/spuRs/ [[alternative HTML version deleted]]