I have a multilevel dataset (individuals within counties) that has missing values. I am ultimately creating a multinomial, multilevel logistic regression model with these data. I am currently using proc mi to ascribe the missing values, then proc glimmix to produce effect estimates, and finally proc mianalyze to pool the estimates from the n=8 imputed datasets. However, I recognize that this fails to account for the clustering in the imputation model. As per Ludtke 2017, "Multiple imputation is a widely recommended means of addressing the problem of missing data in psychological research. An often-neglected requirement of this approach is that the imputation model used to generate the imputed values must be at least as general as the analysis model. For multilevel designs in which lower level units (e.g., students) are nested within higher level units (e.g., classrooms), this means that the multilevel structure must be taken into account in the imputation model." Is there any way to incorporate the multilevel structure into the multiple imputation procedure in SAS?
... View more
