The Hidden Bias of Missing Data in Crisis Standards of Care Simulation Studies: Not So Random, Rethinking Missing...

Introduction: The COVID-19 pandemic highlighted the critical need for robust crisis standards of care (CSC) protocols to handle extreme strain when scarce resources require rationing. Evaluating how such policies might perform in the real world remains paramount; however, to date, study of their performance has been limited to retrospective cohort designs using virtual simulations.^1,2 The Sequential Organ Failure Assessment score (SOFA)-a composite 0-24 score of organ dysfunction incorporating neurologic, pulmonary, cardiovascular, hematologic, hepatobiliary, and renal subscores-remains ubiquitous in nearly all crisis standards of care protocols^3,4,5 despite concerns regarding the utilization and potentially exacerbating existing racial inequities.⁵ Existing simulation studies have handled missing SOFA values by either imputing zero or assuming data are missing at random, followed by complex computational statistical modeling.^6,7 This approach may introduce significant bias, with larger outcome implications than missing data in other forms of medical research, as these values directly affect decisions on who receives life-sustaining therapies. Our study aims to better understand the frequency, structure, and consequence of missing data in CSC simulation studies.