Relaxing Assumptions and Targeted Estimands with MOST

Johannes_Schwenke:

essentially it’s a hidden Markov model (with random slopes) that defines sampling probabilities for each state, for each day, for each patient + an exponential model on top that chooses days each patient is allowed to change states

Random slopes induces a pretty exotic correlation pattern. What I would love to have is a simulated dataset from a Markov-1 model (with possibly random intercepts) where we know we can model that part, but that we still can’t get SOPs right for one state. This may be covered by @philb 's work, and perhaps you have to relax the PO assumption with regard to Y=y-1 to get Y=y right?

@mdebacker - great discussion.