Relaxing Assumptions and Targeted Estimands with MOST

This is a simulated null scenario, so PO for \beta_{tx} is true. The simulation is pretty convoluted, but 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, except for transition to the absorbing state (death), which can always happen.

But I might be misunderstanding your question: allowing for a separate parameter for the first threshold only (between State 1 and State 2) for all basis functions of time also has only minimal impact on marginalized SOPs. So one common OR for all thresholds + one deviation from that OR for threshold one, for all basis functions. In fact the impact is so small that I’m having doubts about the parameters being correctly used in the calculations for the SOPs. I’ll check.