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Re: st: Correlation between 2 variables overtime- accounting for repeated measures


From   megan rossi <megan_rossi@msn.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Correlation between 2 variables overtime- accounting for repeated measures
Date   Sun, 17 Mar 2013 08:49:26 +1000

Yes The surgery was 1-3weeks after the baselines were taken...a fairly strict protocol. Then one year follow up and two year follow up...variable a &b were both low at baseline (still detectable and possibly correlated) but rise following surgery so are high at year 1 and year 2...perhaps best to just combine year 1 and 2 as if they're both high a correlation would be easier to find then if they're both low?

Thanks again 

Sent from my iPhone

On 17/03/2013, at 1:23 AM, "JVerkuilen (Gmail)" <jvverkuilen@gmail.com> wrote:

> On Sat, Mar 16, 2013 at 10:13 AM, megan rossi <megan_rossi@msn.com> wrote:
>> I was thinking of going with xtgee or xtmixed but wasn't quite sure if one would be more appropriate then the other? ( I know gee is more relaxed with equal variance and normality assumptions).
>> With respect to the correlation option I didn't mention that the patients had a uninephrectomy following baseline so I think that disturbs the assumption associated with the ar1 option ie. the association with time is not as straight forward . Do you think given this the unstructured option would be best?
>> 
>> Thanks I will look into SEM too....not sure how that would work, if you know of any websites with an example that would be much appreciated!
> 
> The Stata manual for SEM is pretty good and has numerous examples and
> citations.
> 
> I'm not sure GEE would give you the correlation. xtmixed would let you
> compute an intraclass correlation, which might be what you're looking
> for. You don't have to have equal variances with xtmixed either.
> There's quite a lot of flexibility to specify residual variance
> structures.
> 
> My guess with the surgery is that you need to either model it (as a
> fixed effect) since you do know when it happened, right?
> 
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