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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 00:13:18 +1000

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!

Cheers,

Sent from my iPhone

On 16/03/2013, at 11:46 PM, "JVerkuilen (Gmail)" <jvverkuilen@gmail.com> wrote:

> On Sat, Mar 16, 2013 at 8:32 AM, megan rossi <megan_rossi@msn.com> wrote:
>> Hi All
>> Can you please recommend what syntax would be most appropriate for my below senario
>> 
>> 40 participants with three repeated measures (baseline, year 1, year 2)- observational study ie. no intervention
>> At each of these three time points two continous variables (a) and (b) were measured. I want to know whether (a) and (b) are correlated. If I do a correlation at one time point ie. year 1 the correlation is not significant which I believe is due to the small numbers ie. 40. If I can find a method of accounting for the lack of dependence among these three time points ie. repeated measures I will effectively have 120 pieces of data, which should be sufficient to see a correlation if one really exists.
>> Cheers,
> 
> You could approach this a few different ways. One might be to set up
> the appropriate SEM with correlated residuals to take the longitudinal
> dependence into account. The other would be to set the data up as a
> linear mixed model and use say, AR(1) residuals. I think the SEM
> approach is probably the most straightforward. That said, before you
> do either, be sure to scatterplot both variables broken out over time.
> SEM might wring some extra statistical efficiency, but don't get too
> optimistic.
> 
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