{
"$type": "site.standard.document",
"bskyPostRef": {
"cid": "bafyreihqklv4ctlag2xivxuldwjocwifenj7cnwrjhwfi2crpnw73nbhty",
"uri": "at://did:plc:wwyqal4cnqhuwyacdj7rqq3n/app.bsky.feed.post/3mmbqk75ny4o2"
},
"path": "/t/a-longitudinal-renal-health-outcome-for-clinical-trials-in-acute-kidney-injury/28750#post_11",
"publishedAt": "2026-05-20T10:09:43.000Z",
"site": "https://discourse.datamethods.org",
"tags": [
"formulating sharply defined scientific theories",
"10.7490/f1000research.1113595.1"
],
"textContent": "JohnProwle:\n\n> the scientific question is very much a pragmatic one - how do we understand if a randomised intervention effectively modifies kidney injury/kidney health in early phase studies\n\nThis is, in my view, the very _opposite_ of a scientific question: it is a question rather about **how to industrialise the AKI research enterprise.** The core principle seems to be to deliver a generic outcome definition that liberates researchers from the burden of formulating sharply defined scientific theories specific to the particular intervention being ‘studied’.\n\nJohnProwle:\n\n> however GFR itself is still just a physiological variable indicative of organ function - not directly paralleling structural injury or prognosis\n\nThis is indeed precisely the problem addressed by state-space modeling: there is some latent physiologic state X_t which — we _theorise_ — evolves over time according to some ‘equations of motion’, and this state gets reflected in noisy observations Y_t (or _measurements_) which we may ‘filter’ to recover estimates of the underlying state variables. A basic application of these ideas can be found in this conference poster on tacrolimus dosing [1].\n\n 1. Norris DC, Gohh RY, Akhlaghi F, Morrissey PE. Kalman filtering for tacrolimus dose titration in the early hospital course after kidney transplant. _F1000Research_. 2017;6. doi:10.7490/f1000research.1113595.1\n\n",
"title": "A Longitudinal Renal Health Outcome for Clinical Trials in Acute Kidney Injury?"
}