Relaxing Assumptions and Targeted Estimands with MOST

I have to confess that a fused Lasso is somewhat above my comprehension. mdebacker: > Now, I’m intrigued by your example. Could you specify what is partial in the PPO model in the third column? I fit the model with rcs(time, 6), so five basis functions to model time. The coefficient of each of these basis functions is allowed to deviate linearly from PO across the thresholds, so two parameters per basis function. In the second column, only the first basis function (which, when using a left truncated power basis is the linear effect of time IIRC), is allowed to deviate linearly from PO. mdebacker: > But, to complicate and have fun, I would also constrain the amount of fine-tuning wrt the parent model (overfitting concerns and multiplicity considerations if several children models end up being quite similar). My intuition, after having fit quite a few Markov models on various datasets by now, is that the models will end up very similar, but let’s see.