Dear Anne
In addition to Marc's comments if you are forced to do this then,
assuming your package computes sample size from power then just feed it
a range of powers and find the one for which it calculates the sample
size you had. There is a more elegant way to do this using uniroot but
brute force should work.
Michael
On 26/08/2019 13:42, Marc Schwartz via R-help wrote:
On Aug 26, 2019, at 6:24 AM, CHATTON Anne via R-help <r-help@r-project.org>
wrote:
Hello everybody,
I am trying to accommodate the R codes provided by Donohue for sample size calculation in
the package "longpower" with lmmpower function to estimate the post-hoc power
(asked by a reviewer) of a binary GEE model with a three-way interaction (time x
condition x continuous predictor) given a fixed sample size. In other words instead of
the sample size I would like to estimate the power of my study.
Could anyone please help me to modify these codes as to obtain the power I'm
looking for.
I would really appreciate receiving any feedback on this subject.
Yours sincerely,
Anne
Hi,
Three comments:
1. Don't calculate post hoc power. Do a Google search and you will find a
plethora of papers and discussions on why not, including these:
The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data
Analysis
The American Statistician, February 2001, Vol. 55, No. 1
https://www.vims.edu/people/hoenig_jm/pubs/hoenig2.pdf
Post Hoc Power: Tables and Commentary
https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr378.pdf
Observed power, and what to do if your editor asks for post-hoc power
analyses
http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.html
Retraction Watch:
Statisticians clamor for retraction of paper by Harvard researchers they say
uses a “nonsense statistic”
https://retractionwatch.com/2019/06/19/statisticians-clamor-for-retraction-of-paper-by-harvard-researchers-they-say-uses-a-nonsense-statistic/
PubPeer Comments on the paper cited in the above RW post:
https://pubpeer.com/publications/4399282A80691D9421B497E8316CF6
A discussion on Frank's Data Methods forum also related to the same paper
cited above:
"Observed Power" and other "Power" Issues
https://discourse.datamethods.org/t/observed-power-and-other-power-issues/731/30
2. If you are still compelled (voluntarily or involuntarily), you may want to
review the vignette for the longpower package which may have some insights,
and/or contact the package maintainer for additional guidance on how to
structure the code. See the vignette here:
https://cran.r-project.org/web/packages/longpower/vignettes/longpower.pdf
3. Don't calculate post hoc power.
Regards,
Marc Schwartz
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