Programming the statistical procedures from SAS

GLIMMIX convergence problems with ordinal model

Occasional Contributor
Posts: 6

GLIMMIX convergence problems with ordinal model

Hello, I hope that someone may help me

Well, I am analyzing a longitudinal data set about hearing loss which has 226 subjects with repeated measures each one (with a maximum of 12 observations per subject) over a follow up time of 22.2 years. I don't have fixed points along time, so I am dealing with my time variable as a continuous covariate. The data set is highly unbalanced, but I considered to work under the assumption of missing completely at random. Now, the problem I guess is because of how my response variable is trichotomized. I am using as cut-off points two values given by an international recognized scale for measuring hearing loss, which is giving me the problem of no convergence (SAS shows the message 'Did not converge'). I have tried all the possible options for the covariance structure, but any had worked.

The categories which are "normal", "mild" and "moderate" have 836, 31 and 6 observations respectively. When I change the cut-off points (i.e. for those given by a k-mean clustering), the following code works well:

proc glimmix data=hearing1 method=RSPL;

class id;

model ansi = age age*time/ dist=multinomial link=cumlogit solution;

random intercept age/ subject=id type=CS;


So my question is if the procedure does not converge, is it because of the frequencies in my classification? Should I split more equally balanced the observations?

I will wait for your advice

Thanks in advance


KU Leuven-Student

Respected Advisor
Posts: 2,655

Re: GLIMMIX convergence problems with ordinal model

With that sort of imbalance, and two continuous variables that are likely to have a lot of collinearity (even if age is age at enrollment), I would expect a lot of problems with convergence.  I would say that your approach is more valid for your data than applying the international cutpoints.

One other approach would be two dichotomous analyses: normal vs non-normal, and mild vs moderate (could be normal+mild vs moderate, if you want to keep the same subjects in the two analyses.)

Steve Denham

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