Stopped because of too many likelihood evaluations

Jordani — Fri, 25 Nov 2016 03:18:09 GMT

Hi everybody ,

I have a longitudinal data on Body Mass Index BMI before and after medical intervention . I am running a mixed model that accounts for time interval between date of measurement and the date of intervention. I also included an interaction term.

the data looks like this :

ID BMI Time_interval Time

1 29 -10 Before

1 30 10 After

1 30.5 20 After

2 30.1 -8 Before

2 29.5 11 After

2 28.7 18 After

The model I used :

ods graphics on;

Proc mixed data = have plots= (Maxpoints=none);

class ID Time ;
model BMI= Time Time_interval Time*Time_interval /solution residual outpm=marg outp=cond vciry
influence(iter=0 effect=ID est) ;
random Time Time_interval /subject=pat_mrn_id type=UN g gcorr;
run;
ods graphics off;

I keep receiving this message :

"Stopped because of too many likelihood evaluations"

any ideas ?

Re: Stopped because of too many likelihood evaluations

SteveDenham — Thu, 01 Dec 2016 13:27:44 GMT

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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Stopped because of too many likelihood evaluations

Re: Stopped because of too many likelihood evaluations