I didn’t read @micah as trying to identify individual responders of different degrees as much as just trying to capture that your probability of having some desirable value is higher on treatment than control? For example maybe the mean difference between two groups does not meet MCID but if you fit a PO model you’d find that eg the treatment has a higher proportion of individuals with better scores than control at any given threshold? I suppose you could even go a little further and fit a PO model (or a distributional model) and make decisions based on the difference in probability that Y is greater than some threshold of interest?
I think it’s strange when there’s some absolute threshold on a scale where people can agree that even a 2-5% change would be meaningful (eg because patients find no additional utility when scores are reduced below x) but then a mean difference which implies/supports a risk difference of 2-5% at that threshold could be crudely dismissed as not meaningful?