But does that matter if it is a dependent variable? When you extend the model as in the example there, my understanding is that the ‘missing values’ are treated as parameters - so for missing outcome variables it is similar to a doing a prediction for new data. If you want you could make a model with this parameters included in order to include your uneven outcomes, and then ignore these parameters when interpreting your results… I don’t think it can effect your coefficients other than by allowing you to include all the data