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  "path": "/t/a-longitudinal-renal-health-outcome-for-clinical-trials-in-acute-kidney-injury/28750#post_2",
  "publishedAt": "2026-05-19T17:13:32.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "10.3150/12-BEJSP07",
    "10.1214/14-STS511"
  ],
  "textContent": "Given how you’ve already branded your effort, it may be too late to advise against setting out presumptively to define any One Outcome to Rule Them All. Still, I think it so much more fruitful _scientifically_ to start from scientific questions, and to construct outcomes specifically adapted to them.\n\nIf you wish to advance a unifying perspective, seek this in a general _formalism_ rather than a standard outcome. For such a formalism in this problem setting, I would reach for state-space modeling and particle filtering methods [1,2]. Of all the organs, kidney physiology surely has the richest theoretical basis; the modeling opportunities here should be marvelous. (Also, how could nephrologists not love _filtering_?  )\n\n  1. Künsch HR. Particle filters. _Bernoulli_. 2013;19(4):1391-1403. doi:10.3150/12-BEJSP07\n\n  2. Kantas N, Doucet A, Singh SS, Maciejowski J, Chopin N. On Particle Methods for Parameter Estimation in State-Space Models. _Statist Sci_. 2015;30(3):328-351. doi:10.1214/14-STS511\n\n\n",
  "title": "A Longitudinal Renal Health Outcome for Clinical Trials in Acute Kidney Injury?"
}