Greetings All, I am seeking help with proper interpretation of a proportional odds model I am using (for learning purposes). The outcome of interest is the modified rankin scale (mRS) at 90-days after stroke with values ranging from 0-6. My predictors include the mRS score at 30-days (same 0-6 scale), age, and prior stroke. Using the rms package, my model is as follows:

```
model = lrm(MRS90D ~ MRS30D * rcs(AGE,4) + PMH_STROKE, data=data, x = TRUE, y = TRUE)
The output (summary(model)) is as follows:
Effects Response : MRS90D
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
MRS30D 2 4 2 3.90390 0.18678 3.537800 4.27000
Odds Ratio 2 4 2 49.59600 NA 34.392000 71.52000
AGE 52 70 18 0.49424 0.21579 0.071296 0.91717
Odds Ratio 52 70 18 1.63920 NA 1.073900 2.50220
PMH_STROKE 0 1 1 0.28317 0.18362 -0.076709 0.64305
Odds Ratio 0 1 1 1.32730 NA 0.926160 1.90230
Adjusted to: MRS30D=4 AGE=62
```

I would sincerely appreciate help in the proper interpretation of the odds ratio, particularly for the MRS30D variable. How would this be stated and explained to others (e.g. in a paper or to a clinical collaborator).

Thank you all in advance.

Bill