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

[Jeph Herrin <info@flyingbuttress.net>]

Generally, I usually do these using simulation.  That is, simulate a 
large number of datasets similar to the one you have but with values 
generated by a random process based on a "known" true value. Then to see 
how much power the model has, calculate the proportion of datasets where 
the model rejects the null hypothesis. -xtmixed- will take a while to 
run on all of the datasets, but it's not too bad if you only have one 
effect you want to estimate the power to detect.

hth,
Jeph


On 3/7/2014 2:55 PM, Mohammod Mostazir 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
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