Bayesian Proportion Odds with repeated measures

I’m trying to find the best approach to analyze data that contain multiple follow up measurements. The outcome variable is the Knee injury and Osteoarthritis Outcome Score (KOOS). The KOOS is the mean of numerous Likert items that are converted to a value between 0 and 100. I’ve previously used the Bayesian proportional odds model blrm from the rmsb package. However I’m not sure if this can account for serial data. brm from the brms package can model repeated measures but when using an ordinal model (family = cumulative(“logit”)) gives the following error: Error: Family ‘cumulative’ requires either positive integers or ordered factors as responses.

For clarity this is the model that gives the error
f <- brm( KOOS.Pain ~ s(age) + s(BMI )+ sex + (1| subjectId) + immediate.relief + visit + surgery + KOOS.Pain.baseline, data = d, family = cumulative("logit") )

and this model “works” but I’m not sure if is accounting for the correlation between subject visits

f <- blrm( KOOS.Pain ~ rcs(age, 3) + rcs(BMI, 3) + sex + cluster(subjectId) + Immediate.relief + visit + surgery + KOOS.Pain.baseline, data = d )

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What you want is a first-order Markov ordinal model, see examples by @f2harrell in

The ORBITA-Cosmic trial has also applied this model here

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And now our tutorial article is out:


It is one of the best tutorial papers I have ever read.