{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreibvdmydaljhoaaohm6pbpjfl4skkzmaymv7mi2pdlrk363aiqqfcq",
    "uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3mmupa22wvkh2"
  },
  "path": "/t/relaxing-assumptions-and-targeted-estimands-with-most/28755#post_13",
  "publishedAt": "2026-05-27T22:52:06.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "@philb",
    "@mdebacker"
  ],
  "textContent": "Johannes_Schwenke:\n\n> 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\n\nRandom slopes induces a pretty exotic correlation pattern. What I would love to have is a simulated dataset from a Markov-1 model (with possibly random intercepts) where we know we can model that part, but that we still can’t get SOPs right for one state. This may be covered by @philb 's work, and perhaps you have to relax the PO assumption with regard to Y=y-1 to get Y=y right?\n\n@mdebacker - great discussion.",
  "title": "Relaxing Assumptions and Targeted Estimands with MOST"
}