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  "path": "/t/relaxing-assumptions-and-targeted-estimands-with-most/28755#post_3",
  "publishedAt": "2026-05-24T21:00:53.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "Thanks a lot Frank, there are several interesting points here. Let me try to distinguish them and build on what you wrote:\n\n  * TMLE: while I am very interested in discussing the technique and the fact that it has not delivered on its promises (I think this is very important as I have seen/heard some ideas that the usefulness of TMLE will not lie in the usual tabular covariates in RCTs but rather its ability to also incorporate unstructured raw baseline information (CT-scan, EEG, …) - an idea that I think is scientifically appealing if one is obsessed with being as faithful as possible to the X), I did not mean to focus on that now. Instead, I wondered whether one could borrow at a high level the philosophy: be as good as possible for an intermediate model, and then focus the model back to the estimand of interest (or multiple estimands in my case).\n\n  * That being said, I guess your main point is that there is no shortcut to properly accounting for model uncertainty - a point that is very relevant for the two-model framework I had in mind (parent vs tailored to estimand of interest).\n\n  * I agree with your point about no unique overall attack plan: there is no mathematical shortcut to translating all that is known about the disease process. So, to clarify, my intention is not to develop a one-size-fits-all type of thinking. I will provide an example below so that we can discuss more specifically, narrowing the scope of the topic.\n\n  * I am very happy you highlighted the comment by Phil Boonstra. I think this merits quite some attention and I haven’t digested consequences yet. I agree with your point about differences in the effect of risk factors on terminal events vs on other outcomes driving some part of the consideration of a single model vs joint modeling. But is that all? If I do not expect a difference in the effect of risk factors, and I am interested in the terminal event as well, how would I decide to go for joint modeling rather than a single ordinal model that acknowledges that the terminal event is indeed worse? For the latter, can I buy in any way any protection towards the highlighted issue of neighbor transfer from worst state to adjacent state rather than all relevant states? I suppose this is less of a concern if the terminal event is incidental (eg death in an early neurology study) and I want it to be included in a “or worse” type of summary?\n\n  * I take your point that global assumptions such as proportional odds are often not critical for the kinds of high-level summaries we are interested in in the first place (e.g. overall improvement across outcome levels). So I understand that from that point of view, my intention is not very convincing. However, recall my framework: stakeholders are usually _very_ attached to some definitions of estimands. So I try to start from their familiar estimands, and embed them within a broader modeling framework (e.g. MOST) to stop the massacre about the estimand dictating how we record the Y, rather than asking them to redefine the problem from scratch.\n\n\n\n\nPerhaps an example will make the debate more concrete. Suppose I have a 28 day study where patients experience daily one of four ordinal states:\n\n  1. at home,\n  2. hospitalized,\n  3. on ventilator,\n  4. dead.\n\n\n\nThe current approach in MOST is to say: “let’s model that longitudinal ordinal data, and then you pick the estimand of your choice - globally you should not overreact to assumptions like PO”. Now, I live in a world where usually the estimands are already defined. Suppose I have 2:\n\n  1. The probability to be at home at the end of the study,\n  2. The mean time spent on ventilator or worse.\n\n\n\nWhat is typically done today:\n\n  * the first is analyzed via dichotomization, usually only looking at the time point of interest (don’t throw the rocks at me)\n\n  * the second via counting days in certain states\n\n  * with essentially no connection between the two analyses\n\n\n\n\nHow do we change that? My thinking is that if I promote MOST as such, I will get pushback because of the fear of the impact of global assumptions (eg PO) for the local estimands (risk difference at the specific cutoff “at home”). So I was wondering whether, in order to mitigate that, I should think about a layered approach where models would be “tweaked” (this should be another word that makes it look less frightening wrt pre-specification) for each estimand:\n\n  * For the “at home at day 28” estimand: optimize more local structure, both in time and outcome value (e.g. borrowing more strongly between adjacent clinical states such as “home” and “hospitalized” than between “home” and “death”)\n  * For the “time on ventilator or worse” estimand: as I look at something over the course of the whole study, I could imagine optimizing the time aspect differently than for the first estimand.\n\n\n\nIn the end, I suppose my question is “within a framework that tries to be as faithful as possible to the Y (eg MOST), how do we reconcile fixed, local estimands with a global model? Should we think about adapting/targeting the model to the several estimands?”",
  "title": "Relaxing Assumptions and Targeted Estimands with MOST"
}