Thanks for all of your suggestions. The cause was indeed quasi-seperation due to 0 cells. However, I managed to find some solutions without deleting the offending variable(s). A couple of solutions: 1. This was complex sample survey data being imputed. The offending variables were a combined PSU/Statum variable and location variable used to impute data (along with other variables in the model). Multiple datasets were combined from different geographic locations and so there were many 0 cells when looking at PSUstratum*location, as strata and PSU were specific to each geographic region. The solution was to delete the location variable in the imputation and just use it at the analysis stage, since the PSU/stratum already took location into account. 2. Despite fixing the PSU/Stratum variable, I still got the MLE warning for a binary variable with a rare outcome. The solution to this was to just swich from FCS logistic to FCS discim and use /classeffects=include, to include the classification variables in the imputation.
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