Relaxing Assumptions and Targeted Estimands with MOST

I am not used to developing a sequence of models and always try first to accommodate outcomes in a unified way. My goal is to have a unified model that is flexible enough to be accurate for all the main study goals and not so flexible as to overfit. There are two cohesive “grand strategies” to accomplish that: * Spend a lot of time eliciting priors for parameters representing departures from assumptions. Here this refers mainly to non-proportional odds (partial proportional odds) parameters. * Posit a model that has PO for treatment (but not for time) and a model that allows much different treatment effects for some of the major outcomes. Choose the model that is most likely to predict future patient outcomes better, by choosing between the two models which has lower AIC.