{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ECKSPP26DPNKBZ3GOU4DTYAVHH","short_pith_number":"pith:ECKSPP26","schema_version":"1.0","canonical_sha256":"209527bf5e1bdaa0e766753839e01539fa29640a98512d65523c1be8d96eac6c","source":{"kind":"arxiv","id":"2606.20341","version":1},"attestation_state":"computed","paper":{"title":"Anchors Away: Navigating Unanchored Indirect Comparisons with Multilevel Unanchored Meta-Regression (ML-UMR)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Conor Chandler, Jack Ishak","submitted_at":"2026-06-18T15:10:31Z","abstract_excerpt":"Unanchored indirect treatment comparisons using single-arm studies or disconnected evidence are increasingly used in health technology assessment (HTA) when randomized evidence is unavailable. Existing methods, including matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC), are generally limited to pairwise settings and typically estimate marginal effects in the comparator study population, which may differ from the decision-relevant population.\n  We propose multilevel unanchored meta-regression (ML-UMR), a Bayesian regression framework for synthesizing individ"},"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":"2606.20341","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-18T15:10:31Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"9b95c270385746efae4710ff18b7a2ad47aebc8218bddf62f0336fcbbf047137","abstract_canon_sha256":"d51b5cf805e5afec0b991c438dc28f8ce7d13277771b0d60791075b9fbd1b4ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:13:09.415612Z","signature_b64":"YKV74dqrv0+xw+SddfZT8CDbs77BGpHq43uCzmJSbNw/Wa7nOAaKzK5yQNSZQGOLwImxU7sE/SsLJOleidBQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"209527bf5e1bdaa0e766753839e01539fa29640a98512d65523c1be8d96eac6c","last_reissued_at":"2026-06-19T16:13:09.415235Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:13:09.415235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Anchors Away: Navigating Unanchored Indirect Comparisons with Multilevel Unanchored Meta-Regression (ML-UMR)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Conor Chandler, Jack Ishak","submitted_at":"2026-06-18T15:10:31Z","abstract_excerpt":"Unanchored indirect treatment comparisons using single-arm studies or disconnected evidence are increasingly used in health technology assessment (HTA) when randomized evidence is unavailable. Existing methods, including matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC), are generally limited to pairwise settings and typically estimate marginal effects in the comparator study population, which may differ from the decision-relevant population.\n  We propose multilevel unanchored meta-regression (ML-UMR), a Bayesian regression framework for synthesizing individ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20341","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/2606.20341/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":"2606.20341","created_at":"2026-06-19T16:13:09.415297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20341v1","created_at":"2026-06-19T16:13:09.415297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20341","created_at":"2026-06-19T16:13:09.415297+00:00"},{"alias_kind":"pith_short_12","alias_value":"ECKSPP26DPNK","created_at":"2026-06-19T16:13:09.415297+00:00"},{"alias_kind":"pith_short_16","alias_value":"ECKSPP26DPNKBZ3G","created_at":"2026-06-19T16:13:09.415297+00:00"},{"alias_kind":"pith_short_8","alias_value":"ECKSPP26","created_at":"2026-06-19T16:13:09.415297+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/ECKSPP26DPNKBZ3GOU4DTYAVHH","json":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH.json","graph_json":"https://pith.science/api/pith-number/ECKSPP26DPNKBZ3GOU4DTYAVHH/graph.json","events_json":"https://pith.science/api/pith-number/ECKSPP26DPNKBZ3GOU4DTYAVHH/events.json","paper":"https://pith.science/paper/ECKSPP26"},"agent_actions":{"view_html":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH","download_json":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH.json","view_paper":"https://pith.science/paper/ECKSPP26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20341&json=true","fetch_graph":"https://pith.science/api/pith-number/ECKSPP26DPNKBZ3GOU4DTYAVHH/graph.json","fetch_events":"https://pith.science/api/pith-number/ECKSPP26DPNKBZ3GOU4DTYAVHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH/action/storage_attestation","attest_author":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH/action/author_attestation","sign_citation":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH/action/citation_signature","submit_replication":"https://pith.science/pith/ECKSPP26DPNKBZ3GOU4DTYAVHH/action/replication_record"}},"created_at":"2026-06-19T16:13:09.415297+00:00","updated_at":"2026-06-19T16:13:09.415297+00:00"}