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

[Lance Erickson <lance_erickson@byu.edu>]

Jeph,

Is the content at this link to the UCLA help pages (http://www.ats.ucla.edu/stat/stata/code/simulate.htm) a reasonable example of the approach you take? I'm referring to the first of the two examples they show.

By '"known" true value' do you mean the coefficients estimated from the existing data?

Thanks,
Lance

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jeph Herrin
Sent: Friday, March 07, 2014 1:29 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Posthoc power analysis for linear mixed effect model

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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