You might want to try a conditional model using PROC GLIMMIX, but if all the missingness is in the dependent variable, that probably will not help much. With responses that are MAR (missing at random), your choices come down to fitting the complete case data only, or estimating the response level in the missing response cases using the OUTPUT statement to get predicted values for the missing responses, and replacing the missing values with the most likely level and then refitting the model. You could also look into using PROC SCORE to estimate the missing responses, but that looks pretty rocky to me, given that your response is an ordinal level.
SteveDenham
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