Relaxing Assumptions and Targeted Estimands with MOST

Thanks for this interesting thread @mdebacker. mdebacker: > * First, represent the data using MOST, to capture the patient trajectories as faithfully as possible. The components would be the ones that allow me to go back to primary and secondary in usual trials (along all relevant intercurrent events). > * Second, fit an intermediate (“parent”) model that is optimized for these trajectories as a whole, and thus naturally introduces structural assumptions that allow information to be borrowed across time points and outcome levels. > I’m wondering when we/you would say a MOST is faithful enough to the raw trajectories / the raw SOPs. What criteria do we use? E.g., in the example below, the marginal probability of being in state 1 is underestimated by the model at all time points, sometimes quite substantially, and the probability of being in state 2 is being overestimated. (If you look closely at the plots in @f2harrell 's and Max Rohde’s paper on MOST, this happened in the ACTT-2 data as as well, just not as much). I haven’t managed to relax the model in a way that still converges but results in substantially less bias in the SOPs (or there’s a mistake in my code): If we are interested in a difference between groups in time spend in state k, would you abandon MOST?