The fact that your subjects are not measured at the same timepoints complicates things immensely.
It looks to me as if you are trying to fit BMI to time_interval, but that there is something unusual about the pre-intervention values. Using an unstructured covariance matrix with so many time_interval values, and with not every subject at those times leads to definite convergence problems.
If all of your data looks like this for the first two IDs, I might consider recoding time_interval to something like:
data want;
set have;
if time_interval<=0 then era=0;
if 0<time_interval<=10 then era=1;
if 10<time_interval<20 then era=2;
...more like this if needed...
run;
Then I would try to fit the following model:
Proc mixed data = want plots= (Maxpoints=none);
class ID era ;
model BMI= era /solution residual outpm=marg outp=cond vciry
influence(iter=0 effect=ID est) ;
random intercept/subject=ID;
repeated era /subject=ID type=ARH(1) g gcorr;
run;
Now if you really don't like aggregating time_interval like this, I would suggest fitting some sort of spline, using the EFFECT statement.
Steve Denham
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