{
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  "path": "/t/external-validation-of-logistic-based-risk-in-time-to-event-setting/28752#post_1",
  "publishedAt": "2026-05-20T18:30:28.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "https://www.acpjournals.org/doi/10.7326/M22-0844"
  ],
  "textContent": "I am working on validating a 1-year risk of an event in EMR data. The previously developed model (which is binary logistic) is already integrated in the EMR, which calculates 1-year risk of the event every time a patient has a visit (using info from that visit and previous 180 days). But a patient could have several visits in a year and each time the risk is calculated. So, what I have is a patient-visit data with likely different risk scores at different visits. I am basically censoring the patient-visit observation whenever next visit happens and a new predicted risk is added. How do I go about validating these one-year risks in this time-to-event type of setting with censoring? Do I:\n\n  1. Follow the external validation techniques for survival model (following for example, this paper: https://www.acpjournals.org/doi/10.7326/M22-0844). So, converting 1-year risk to risk at each follow-up time for each observation (assuming constant hazard), and estimating O/E, calibration slope/plot, Harrell’s c-statistic..; OR\n\n  2. Do I use inverse probability of censoring weights (IPCW) and apply the approaches for discrimination, calibration for binary prediction with the IPCW weights?\n\n\n\n\nI am struggling to figure out what the right approach is in such situations.",
  "title": "External validation of logistic-based risk in time-to-event setting"
}