{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5DZDJAGOGGANBS25OQRPR7PT4S","short_pith_number":"pith:5DZDJAGO","schema_version":"1.0","canonical_sha256":"e8f23480ce3180d0cb5d7422f8fdf3e49b52575d4b64d8df84e4f330e4a9d2de","source":{"kind":"arxiv","id":"2509.10383","version":1},"attestation_state":"computed","paper":{"title":"Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ayman Sadek (1), Bristol, David M. Phillippo (1), Hugo Pedder (1), Nicky J. Welton (1) ((1) University of Bristol, UK)","submitted_at":"2025-09-12T16:17:11Z","abstract_excerpt":"Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in non-proportional hazards when comparing treatments from different classes. As a result, alternative m"},"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":"2509.10383","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-09-12T16:17:11Z","cross_cats_sorted":[],"title_canon_sha256":"53b32aa47d21efcdedffe6e1a2189e0cd2c176aa181ea4a5d72ddd7246d1e71f","abstract_canon_sha256":"3296a299c800b1a75d86fc2ec055b9f71f81afc40977ac8da4c7711dc3f0c098"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:11:08.924893Z","signature_b64":"rrvvWb7RlpAzJcJkunbk0MgIfQ4rYHuJbYFyEVUdeuwH948XYDLfX/0L4q19OsHGCw1J9DbhMaC1j/B4xZWcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8f23480ce3180d0cb5d7422f8fdf3e49b52575d4b64d8df84e4f330e4a9d2de","last_reissued_at":"2026-07-05T12:11:08.924387Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:11:08.924387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ayman Sadek (1), Bristol, David M. Phillippo (1), Hugo Pedder (1), Nicky J. Welton (1) ((1) University of Bristol, UK)","submitted_at":"2025-09-12T16:17:11Z","abstract_excerpt":"Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in non-proportional hazards when comparing treatments from different classes. As a result, alternative m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.10383","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/2509.10383/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.10383","created_at":"2026-07-05T12:11:08.924455+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.10383v1","created_at":"2026-07-05T12:11:08.924455+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.10383","created_at":"2026-07-05T12:11:08.924455+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DZDJAGOGGAN","created_at":"2026-07-05T12:11:08.924455+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DZDJAGOGGANBS25","created_at":"2026-07-05T12:11:08.924455+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DZDJAGO","created_at":"2026-07-05T12:11:08.924455+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20567","citing_title":"Meta-analysis and network meta-analysis of time-to-event outcomes with non-proportional hazards: a Bayesian time-varying hazard ratio approach","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S","json":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S.json","graph_json":"https://pith.science/api/pith-number/5DZDJAGOGGANBS25OQRPR7PT4S/graph.json","events_json":"https://pith.science/api/pith-number/5DZDJAGOGGANBS25OQRPR7PT4S/events.json","paper":"https://pith.science/paper/5DZDJAGO"},"agent_actions":{"view_html":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S","download_json":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S.json","view_paper":"https://pith.science/paper/5DZDJAGO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.10383&json=true","fetch_graph":"https://pith.science/api/pith-number/5DZDJAGOGGANBS25OQRPR7PT4S/graph.json","fetch_events":"https://pith.science/api/pith-number/5DZDJAGOGGANBS25OQRPR7PT4S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S/action/storage_attestation","attest_author":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S/action/author_attestation","sign_citation":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S/action/citation_signature","submit_replication":"https://pith.science/pith/5DZDJAGOGGANBS25OQRPR7PT4S/action/replication_record"}},"created_at":"2026-07-05T12:11:08.924455+00:00","updated_at":"2026-07-05T12:11:08.924455+00:00"}