Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs
Survival models can model time-to-event outcomes using partially observed data. They are widely used in clinical prediction, including cancer risk, disease progression, treatment response, and mortality. Recent models often rely on rich inputs collected at a specific clinical encounter, such as medical images, laboratory tests, electronic health record snapshots, or sensor measurements. In large retrospective datasets, these inputs are usually collected over many calendar years. As a result, they may contain clues about when they were acquired through changes in devices, protocols, documentation, patient mix, or clinical practice. This creates a potential failure mode when outcomes are ob...
| Authors | Yanqi Xu, Hui Dai, Carlos Fernandez-Granda et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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