This is no different than in an ordinary logistic model - the more variables you add to the model, the more sparse the data become (just like adding more dimensions in a multi-way table with a fixed amount of data). When the data become too sparse, the result is that some model parameters are actually infinite. Recall that the parameters in a logistic model are related to odds ratios and an odds ratio in a table with a zero count can be infinite. Obviously, an iterative estimation method (like maximum likelihood or the GEE algorithm) will not converge in that case.
However, moving the added variable from the model to SUBJECT= in the REPEATED statement changes the model and the assumptions you are making. The SUBJECT= effect is not part of the model being estimated - it simply defines which observations in the data are considered correlated as described in this note. Changing the SUBJECT= effect changes which observations are considered correlated and might be incorrect if done without thought.
That said, if COUNTY does not belong in SUBJECT=, and if you do want to estimate its effect as part of the model, they you might be able to include the variable in the model if you otherwise reduce the number of parameters in the model. You could do that in many ways such as simply merging levels within County or any other CLASS variable, or of course, by removing other variables from the model.
And by the way, if it *is* appropriate to add County in SUBJECT=, then this can be done by simply specifying ID*COUNTY or ID(COUNTY) as appropriate. There is no need to create a new variable that combines ID and COUNTY. Again, this is described in more detail in the note referred to above.
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