Robustifying and Selecting Cohort-Appropriate Prognostic Models under Distributional Shifts
External validation is widely regarded as the gold standard for prognostic model evaluation. In this study, we challenge the assumption that successful external calibration guarantees model generalizability and propose two complementary strategies to improve transportability of prognostic models across cohorts. Using six real-world surgical cohorts from tertiary academic centers, we tested whether successful external calibration depends largely on similarity in covariates and outcomes between training and validation cohorts, quantified using Kullback-Leibler (KL) divergence, with calibration assessed by the Integrated Calibration Index (ICI). From the model-developer's perspective, we tra...
| Authors | Dimitris Bertsimas, Carol Gao, Angelos G. Koulouras et al. |
|---|---|
| Primary category | stat.ME |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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