The C-index illusion: discrimination without calibration in published survival models

תקציר מקורי באנגליתarXiv:2607.19526v1 Announce Type: new Abstract: "Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.e. the concordance index, produces systematically misleading model comparisons, because the metric ignores calibration and time-dependent accuracy. Whether this matters for real, published, non-clinical models has not been tested. We reproduce three published survival-ML models across three structurally distinct domains (hard-drive failure, peer-to-peer credit default, and user disengagement on digital platforms), validate our evaluation instrument against the anchor paper's own synthetic experiment, and test five pre-registered hypotheses under a Holm-corr