I'm not a SAS expert, but I have some experience with longitudinal data analysis. One common approach for analyzing longitudinal data with ranked or nominal outcomes is to use generalized estimating equations (GEEs). GEEs allow you to model the longitudinal changes in your outcomes while accounting for the within-subject correlation over time. Another option could be to use mixed-effects models or random-effects models, which also account for the correlation between repeated measurements. Also, if you're interested in learning more about data analysis in general, there are some great Power BI courses available online. They can help you learn things way faster than you would by yourself.
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