I have not seriously investigated how the PPO model is fit using the GENMOD procedure. However, I do know that you have to employ an altered construction of the data and that PPO model estimation employing the GENMOD procedure then requires a GEE model to attempt to account for the covariance of response levels which arises from a multinomial distribution. But the GEE only approximates the covariance of the response levels. Moreover, the GEE which is employed to account for the covariance arising from a multinomial response requires specification of cow as the subject. But you wanted to use a GEE to account for correlations between cows due to clustering in herds. Thus, the PPO model fit employing the GENMOD procedure does not allow appropriate modeling of all of the correlations which appear in the expanded data.
The NLMIXED procedure does not require an altered data construction AND it does fit the multinomial model. Covariances between response levels are accounted for in the likelihood maximization process. Moreover, you can incorporate random herd effects to account within-herd correlated responses. When you fit the PPO (and PO) models employing NLMIXED, you test the proportional odds assumption employing a likelihood ratio test rather than a score test. I have not studied score and likelihood ratio tests for testing the proportional odds assumption. However, my guess is that a likelihood ratio test would have better properties than the score test.
I can't guarantee that you won't have problems fitting the PPO model employing the NLMIXED procedure. However, the NLMIXED procedure has much going for it over the GENMOD procedure for estimating a PPO model.
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