@desireatem wrote:
_c1=20202020202020202020203120202020 _c2=20202020202020202020202020202020 _c3=20202020202020202020202020202020 _c4=20202020202020202020202020202020 _c5=20202020202020202020202020202020 NOTE: There were 230 observations read from the data set WORK.STROKE. WHERE CMISS(age, gender, race_ethnic, pay_source, cmg_tier, report_cmg, weight_cmg, expected_los)=0;
Thanks for this helpful information. So, the third bullet point of my list of potential issues described what happened: Missing values of one or more other predictor variables in the observations with deposi=0 have made ref="0" an invalid reference level specification for deposi. The values of _c1, ..., _c5 indicate that all observations used in the analysis have deposi=1. Therefore, ref="1" is the only possible reference value for deposi. But even this is actually useless because the parameter estimate of the constant variable deposi will be zero, i.e., you can remove deposi from the CLASS and MODEL statements without losing more information.
Given that 72.5% of the 837 observations in work.stroke (with non-missing deposi) have been excluded because of missing values in age, gender, race_ethnic, pay_source, cmg_tier, report_cmg, weight_cmg and/or expected_los, you should check if
that many values are correctly missing
most of the missing values are from a single variable (or from only two, ...) -- Reeza's suggestion will be helpful for this
you can remove those largely missing variables from the model or impute their missing values.
Otherwise, you won't get a very useful model from PROC GENMOD as it would be based on a relatively small, not representative subset of your analysis dataset, disregarding information (e.g., about the relationship between deposi and mrs_discharge) which you could gain from the rest of the dataset.
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