Re: Mixed Model Repeated Measures

Posted by Bruce Weaver on Sep 26, 2018; 1:20am
URL: http://spssx-discussion.165.s1.nabble.com/Mixed-Model-Repeated-Measures-tp5726021p5736705.html

Ryan has given a good answer to your question.  This is an aside.  Note that
when you are using MIXED, there is no need to compute a timesq variable,
because you express it as time*time on the FIXED sub-cmmand.  

MIXED DV with cen_BaseDV group time
    /FIXED= cen_BaseDV   group time time*cen_BaseDV   time*group time*time
|
SSTYPE(3)

HTH.



parisec wrote

> Since this is thread on mixed models with repeated measures, i figured i'd
> place this here versus starting a new one
>
> I'm running a repeated measure analysis using mixed models. There are lots
> of missing data so mixed models makes the most sense. The study is a
> randomized trial of group
> (placebo = 0 and drug = 1) with a centered baseline DV as a covariate and
> followups for this DV each year for 5 years (evenly timed).
>
>
> MIXED DV with cen_BaseDV group time timesq
>     /FIXED= cen_BaseDV   group time time*cen_BaseDV   time*group timesq  |
> SSTYPE(3)
>   /METHOD=REML
>   /PRINT=G  SOLUTION TESTCOV
>  /Repeated  = time | SUBJECT (subjectid) COVTYPE (AR1).
>
> The above appears to make the most sense for the data including the
> covariance structure. But, my question and dilemma is the whether a random
> intercept needs to be included and how does one determine the covariance
> structure for this?
>
> This statement:
> /RANDOM intercept time | SUBJECT(subjectid) COVType(AR1)
>
> ...with the above does not converge but some other covariance structures,
> like diagonal, do converge and lower the akaike compared with the model
> without the /random intercept.
>
> But, i can't quite make the decision as to whether or not it needs to be
> included and if so, how to determine the covariance structure. For the
> repeated factor, it was very clear from correlations and covariances over
> time that the AR1 makes sense.
>
> can someone recommend some diagnostics, pictures, or tables that might
> help
> me make these decisions?
>
> Thanks
> Carol
>
>
>
>
>
>
>
>
> --
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Bruce Weaver
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