{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreigihmuwiazetdxdhyhtmnbkjqinxyy2srojxb6vyyxfp3rw34jfoi",
    "uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3mmabl6rk3bl2"
  },
  "path": "/t/a-longitudinal-renal-health-outcome-for-clinical-trials-in-acute-kidney-injury/28750#post_4",
  "publishedAt": "2026-05-19T19:51:26.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "I’m curious about the logic of your categories given that you’re transforming several continuous and categorical variables into a 7-point scale. How can you be confident that a given level of urine output represents a worse degree of kidney injury than a certain change in a biomarker?\n\nAnother approach could be treating the best objective measure (or proxy) of AKI as a continuous variable analyzed ordinally. Let’s take urine output as an example. You could say that anything above a certain threshold (like 0.5 ml/kg/hr) is normal and anything lower than that would be worse, ending up with ordered categories like: \\ge 0.5\\, ml/kg/hr,\\, 0.49 ml/kg/hr\\, \\dots 0 ml/kg/hr. Various interventions or death could be categories that are worse than no urine output alone.\n\nI don’t know how doable such a thing is computationally, but I’m wondering if something like this might better address the categorization problem.",
  "title": "A Longitudinal Renal Health Outcome for Clinical Trials in Acute Kidney Injury?"
}