Comparing Sensitivity of Models to Missing Data in the GMANOVA

It is important that a model has a satisfactory fit to the data we analyze. Other general acceptable features of a model are its simplicity and interpretability of parameters. Longitudinal data arising in epidemiologic or clinical studies, for example, are rarely complete. It is therefore desirable that a model is, to some extent, robust to missing data. Thus models should be compared also with respect to this property.