{"paper":{"title":"Survival analysis under label shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ingrid Van Keilegom, Yanyuan Ma, Yuxiang Zong","submitted_at":"2025-06-26T12:49:02Z","abstract_excerpt":"Let P represent the source population with complete data, containing covariate $\\mathbf{Z}$ and response $T$, and Q the target population, where only the covariate $\\mathbf{Z}$ is available. We consider a setting with both label shift and label censoring. Label shift assumes that the marginal distribution of $T$ differs between $P$ and $Q$, while the conditional distribution of $\\mathbf{Z}$ given $T$ remains the same. Label censoring refers to the case where the response $T$ in $P$ is subject to random censoring. Our goal is to leverage information from the label-shifted and label-censored sou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21190","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.21190/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}