Re: st: Posthoc power analysis for linear mixed effect model

[Jeph Herrin <info@flyingbuttress.net>]

Yes, but:

*  Unless one calculates the curve as you have, one will not know
   the power that corresponds to the p-value; and,
*  Most often, one wants to know the power to detect a true effect,
   not the observed effect, in which case one cannot infer anything
   from the observed effect or the p-value.

No?

Jeph


On 3/7/2014 4:43 PM, Joerg Luedicke wrote:
> I'd recommend to not do that at all because a post-hoc power analysis
> is a fairly useless endeavor, to say the least. The reason for that is
> that the "observed" power, i.e. the calculated power that you obtain
> by using the estimates from your model, is a 1:1 function of the
> p-values of these estimates. Therefore, calculating post-hoc power
> doesn't add any information to what you already have! See Hoenig &
> Heisey (2001) for an account on this. Below is an example where we
> repeatedly compare means between two groups and store the "observed"
> power and p-value from each comparison, then plot power as a function
> of p-value:
>
> * ---------------------------------
> cap program drop obsp
> program define obsp, rclass
>
> drop _all
> set obs 200
> gen x = mod(_n-1,2)
> gen e = rnormal()
> gen y = 0.1*x + e
>
> ttest y, by(x)
> local p = r(p)
> local m1 = r(mu_1)
> local m2 = r(mu_2)
> local sd1 = r(sd_1)
> local sd2 = r(sd_2)
>
> power twomeans `m1' `m2' , sd1(`sd1') sd2(`sd2') n(200)
> return scalar p = `p'
> return scalar power = r(power)
> end
>
> simulate power = r(power) p = r(p) , reps(100) seed(1234) : obsp
>
> scatter power p, connect(l) sort ///
> ytitle(`""Observed" power"') ///
> xtitle("p-value")
> * ---------------------------------
>
> Joerg
>
> Reference:
> Hoenig, M & DM Heisey (2001): The Abuse of Power: The Pervasive
> Fallacy of Power Calculations for Data Analysis. The American
> Statistician 55(1): 1-6.
>
>
>
>
> On Fri, Mar 7, 2014 at 2:55 PM, Mohammod Mostazir <mostazirwho@gmail.com> wrote:
> > Dear great stat-warriors,
> >
> > I need some Stata related H--E--L--P here. I have a dataset that has
> > repeated BMI
> > (Body Mass Index; continuous scale) measurements of 10 equally spaced
> > annual time points from 140 cases. The interest is to observed change
> > in BMI in relation to other time-constant and time-varying
> > co-variates. The analysis I have carried out is linear mixed effect
> > model using Stata's 'xtmixed' command with random intercepts and
> > slopes.  Now I would like to carry out a posthoc power analysis to see
> > how much power the study has. Is there any light in Stata in relation
> > to this? I have seen Stata's ''power repeated'' command which does not
> > suit here as they are suitable for one/two way repeated ANOVA designs.
> >
> > Any comment is highly appreciated. Thanks for reading.
> >
> > Best,
> >
> > Mos
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/