Dear Statalist,

Hope you're all well.

I have a dataset with repeated measures of the modified Rankin Scale (mRS), which is an ordinal measure (0 = independence to 6 = death) and I am attempting to find associations between baseline demographic variables (e.g. sex, age, comorbidities) with the mRS at Day 365.

Using the repeated measures of mRS (Day 1, 28, 90, 180, 365), I am attempting to show that there is a benefit to using the additional data, as opposed to just using Day 365 data.
- My current approach was to use meologit in STATA using Day 365 data only, and then comparing to meologit using Day 1, 28, 90, 180, 365.
- I then compared odds ratios and the McKelvey & Zavoinas R2.

However, the journal I have submitted to has rejected this statistical approach on the grounds that it violates the proportional odds assumption and that logistical odds was not the right approach.
- previous attempts at using partial proportional odds and generalised logistic regression did not work due to the number of parameters and struggle to converge / production of negative probabilties.

Searching for alternatives, I have reviewed the literature and come across the Wilcoxon Mann Whitney Generalised Odds Ratios
- Churilov L, Arnup S, Johns H, Leung T, Roberts S, Campbell BC, Davis SM, Donnan GA. An improved method for simple, assumption-free ordinal analysis of the modified Rankin Scale using generalized odds ratios. Int J Stroke. 2014 Dec;9(8):999-1005. doi: 10.1111/ijs.12364. Epub 2014 Sep 4. PMID: 25196780.

I think that this may hold promise, however I will have to stratify the odds ratios in STATA (I think using somersd, as ranksum cannot support this).

However I am unsure how to use the Wilcoxon Mann Whitney Generalised Odds Ratios in a repeated measures analysis.
- Would I stratify by time and adjust for all variables?
- It seems as though that the vanElten method can only adjust for 1 additional strata. I am unsure whether or not somersd can adjust for multiple strata.

Would be very grateful for any insights into alternative approaches for finding associations using repeated measures of ordinal data; and if the generalised odds ratio approach would be suitable.

Many thanks.