Programming the statistical procedures from SAS

GLIMMIX for repeated measures and multinomial (ordered) response

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Occasional Contributor
Posts: 14

GLIMMIX for repeated measures and multinomial (ordered) response

Looking for some interpretation help with a GLIMMIX output (as most the documentation is from logistic models).

The very basic bivariable model is as follows:

proc glimmix data=have noclprint noitprint method=quad gradient ;

  class study_id ;

  model score=race / solution dist=mult link=clogit ;

  random intercept / type=un subject=study_id ;

      covtest 'Need Random Intercept?' 0 ;

  where race ^in(.,88,99) ;

run ;

I have 5500 observations, with a total of 1795 individuals (study_id) observed over time. Participants have multiple scores (range: 0-4) over time. I have made no assumption about the distance between adjacent levels, but they are ordered (i.e. 0=not present, 4=very bad levels present). Race is ethnic race (i.e. caucasian, African-American etc) with 5 levels. I am modeling the probabilities of levels of score having lower ordered values in the response profile table.

Once run, I have 4 intercepts  (0-3), and 5 estimates for race. Numbers below are just made up.

Effect           Score     Race     Estimate

Intercept      0                                  0.5

Intercept      1                                   0.6

Intercept      2                                   0.7

Intercept      3                                   0.8

Race                        Race1               0.09

Race                         Race 2              0.1

Race                          Race3               0.11

Race                          Race4               0.12

Race                          Race5               0.13

I would like to be able to interpret this (eventually) in a multivariable model as well, then present it in a logical and meaningful way. Any direction would be appreciated.    

Respected Advisor
Posts: 2,655

Re: GLIMMIX for repeated measures and multinomial (ordered) response

The key will be to translate the estimates into odds ratios that describe what is going on, and that isn't always easy.  This is a case where ESTIMATE statements are critical to calculating customized odds ratios.  Do a search of this site on 'glimmix multinomial estimate' as I am sure there are answers out there by people who are better at this than I am.

Steve Denham

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