{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZL72ZSJYS5CDZEHVTOSAG6X7ZA","short_pith_number":"pith:ZL72ZSJY","schema_version":"1.0","canonical_sha256":"caffacc93897443c90f59ba4037affc8185479f822e1e36545c621d7e62c3f26","source":{"kind":"arxiv","id":"1901.04599","version":3},"attestation_state":"computed","paper":{"title":"An Ensemble Method for Interval-Censored Time-to-Event Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Halina Frydman, Jeffrey S. Simonoff, Weichi Yao","submitted_at":"2019-01-14T22:52:55Z","abstract_excerpt":"Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this paper we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.04599","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-01-14T22:52:55Z","cross_cats_sorted":[],"title_canon_sha256":"80a7b16327eee2f701e762726629df23af76899b4b557ef3f9e153a9ffea83d3","abstract_canon_sha256":"0fa034b0705a44a12fe531bfd7db7314e12a41973604bcf39bc5e60715e7b92c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:37.341008Z","signature_b64":"uVh5xst6tIGUT43TyN4JhaaOR/k7JE2la89hjz50DlLqeeC5sXK4MHHYDK6vGY9kF55LOG43IwFXqivDyLKTDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"caffacc93897443c90f59ba4037affc8185479f822e1e36545c621d7e62c3f26","last_reissued_at":"2026-05-17T23:43:37.340362Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:37.340362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Ensemble Method for Interval-Censored Time-to-Event Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Halina Frydman, Jeffrey S. Simonoff, Weichi Yao","submitted_at":"2019-01-14T22:52:55Z","abstract_excerpt":"Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this paper we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.04599","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.04599","created_at":"2026-05-17T23:43:37.340473+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.04599v3","created_at":"2026-05-17T23:43:37.340473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.04599","created_at":"2026-05-17T23:43:37.340473+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZL72ZSJYS5CD","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZL72ZSJYS5CDZEHV","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZL72ZSJY","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA","json":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA.json","graph_json":"https://pith.science/api/pith-number/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/graph.json","events_json":"https://pith.science/api/pith-number/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/events.json","paper":"https://pith.science/paper/ZL72ZSJY"},"agent_actions":{"view_html":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA","download_json":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA.json","view_paper":"https://pith.science/paper/ZL72ZSJY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.04599&json=true","fetch_graph":"https://pith.science/api/pith-number/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/graph.json","fetch_events":"https://pith.science/api/pith-number/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/action/storage_attestation","attest_author":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/action/author_attestation","sign_citation":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/action/citation_signature","submit_replication":"https://pith.science/pith/ZL72ZSJYS5CDZEHVTOSAG6X7ZA/action/replication_record"}},"created_at":"2026-05-17T23:43:37.340473+00:00","updated_at":"2026-05-17T23:43:37.340473+00:00"}