{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6JBKBXCPMNPHT3GARESH5EBNR7","short_pith_number":"pith:6JBKBXCP","schema_version":"1.0","canonical_sha256":"f242a0dc4f635e79ecc089247e902d8ffd2905d8cd3fa98de1c31f9eac1d6640","source":{"kind":"arxiv","id":"2212.09903","version":1},"attestation_state":"computed","paper":{"title":"Prognostic Covariate Adjustment for Binary Outcomes Using Stratification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Alejandro Schuler, Alyssa M. Vanderbeek, David P. Miller, Jessica L. Ross","submitted_at":"2022-12-19T22:51:51Z","abstract_excerpt":"Covariate adjustment and methods of incorporating historical data in randomized clinical trials (RCTs) each provide opportunities to increase trial power. We unite these approaches for the analysis of RCTs with binary outcomes based on the Cochran-Mantel-Haenszel (CMH) test for marginal risk ratio (RR). In PROCOVA-CMH, subjects are stratified on a single prognostic covariate reflective of their predicted outcome on the control treatment (e.g. placebo). This prognostic score is generated based on baseline covariates through a model trained on historical data. We propose two closed-form prospect"},"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":"2212.09903","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-12-19T22:51:51Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"bf400974a24a70c724ad1b088d2be2221a6353ac3512e0c00ba22267337cd74d","abstract_canon_sha256":"46f5749dcb96302bbeb27b22a4092b7aca4a067718067e9bdca831ea5d960d6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:01.371580Z","signature_b64":"E+odU7i/cNGxa0ks2Dq6sp61yJozQ+xfACHx7lgF3cilAVh4wTJi1k6/IYUgNMZTfB1Qn3eFLwTub4TX30HvAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f242a0dc4f635e79ecc089247e902d8ffd2905d8cd3fa98de1c31f9eac1d6640","last_reissued_at":"2026-07-05T05:27:01.371038Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:01.371038Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prognostic Covariate Adjustment for Binary Outcomes Using Stratification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Alejandro Schuler, Alyssa M. Vanderbeek, David P. Miller, Jessica L. Ross","submitted_at":"2022-12-19T22:51:51Z","abstract_excerpt":"Covariate adjustment and methods of incorporating historical data in randomized clinical trials (RCTs) each provide opportunities to increase trial power. We unite these approaches for the analysis of RCTs with binary outcomes based on the Cochran-Mantel-Haenszel (CMH) test for marginal risk ratio (RR). In PROCOVA-CMH, subjects are stratified on a single prognostic covariate reflective of their predicted outcome on the control treatment (e.g. placebo). This prognostic score is generated based on baseline covariates through a model trained on historical data. We propose two closed-form prospect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09903","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/2212.09903/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":"2212.09903","created_at":"2026-07-05T05:27:01.371110+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09903v1","created_at":"2026-07-05T05:27:01.371110+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09903","created_at":"2026-07-05T05:27:01.371110+00:00"},{"alias_kind":"pith_short_12","alias_value":"6JBKBXCPMNPH","created_at":"2026-07-05T05:27:01.371110+00:00"},{"alias_kind":"pith_short_16","alias_value":"6JBKBXCPMNPHT3GA","created_at":"2026-07-05T05:27:01.371110+00:00"},{"alias_kind":"pith_short_8","alias_value":"6JBKBXCP","created_at":"2026-07-05T05:27:01.371110+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23246","citing_title":"Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7","json":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7.json","graph_json":"https://pith.science/api/pith-number/6JBKBXCPMNPHT3GARESH5EBNR7/graph.json","events_json":"https://pith.science/api/pith-number/6JBKBXCPMNPHT3GARESH5EBNR7/events.json","paper":"https://pith.science/paper/6JBKBXCP"},"agent_actions":{"view_html":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7","download_json":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7.json","view_paper":"https://pith.science/paper/6JBKBXCP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09903&json=true","fetch_graph":"https://pith.science/api/pith-number/6JBKBXCPMNPHT3GARESH5EBNR7/graph.json","fetch_events":"https://pith.science/api/pith-number/6JBKBXCPMNPHT3GARESH5EBNR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7/action/storage_attestation","attest_author":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7/action/author_attestation","sign_citation":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7/action/citation_signature","submit_replication":"https://pith.science/pith/6JBKBXCPMNPHT3GARESH5EBNR7/action/replication_record"}},"created_at":"2026-07-05T05:27:01.371110+00:00","updated_at":"2026-07-05T05:27:01.371110+00:00"}