Checking model assumptions for a mixed effects cumulative logit model

[juan]

Hi all!

I am analyzing a repeated ordinal measures  data set (4 categories) by fitting a mixed effects cumulative logit model using NLMIXED or GLIMMIX to model between and within clusters variation and make inference on the population. I can't manage to get the residuals in either procedure and hence to check the error assumptions of the logistic distribution. Also, I can't figure how to check the odds proportionality assumption in a mixed model set up for longitudinal measurements.

below is the code I am using but it won't do it.

proc glimmix data=name method=quad;

class group id;

model score = day group/ s link=cumlogit dist=multinomial cl;

random int / sub=id s cl;

output out=new pred=p resid=r;

covtest GLM;

ods output Solutionr=solr ;

run;

Does anybody know what is the best way to test assumptions for this model?

Thanks!

Juan

[SteveDenham]

Difficulties I see, without having a good dataset to test this on:

1. The multinomial distribution option in GLIMMIX does not support calculation of BLUEs (LSMEANS), so residual calculation may be similarly not supported.  Maybe someone else has better information, but this is my best guess as to why you aren't getting residuals.

2. You are not modeling the repeated nature of the data in the current code.  The RANDOM statement should look something like:

random day/residual sub=id type=cs (for example only, you may have a better idea of what covariance structure fits your data).  You will also have to include day in the CLASS statement.

3. If you include R side effects like this, then method=quad will not work--the only available methods are the pseudo-likelihood methods.

4. To get odds ratios, add oddsratio to your model statement.  Perhaps this will give you the info you need to check the proportionality assumption.

Good luck, and keep posting on this.  I am facing a similar challenge, and think I can learn from what happens here.

Steve Denham