{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:F4MF35HN6HYJ7PRV5LGRVNIGRS","short_pith_number":"pith:F4MF35HN","schema_version":"1.0","canonical_sha256":"2f185df4edf1f09fbe35eacd1ab5068cb838f8e5d505b1ec6e1a42a5a54c42f3","source":{"kind":"arxiv","id":"2310.18027","version":4},"attestation_state":"computed","paper":{"title":"Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.ME","authors_text":"Alyssa M. Vanderbeek, Arman Sabbaghi, Charles K. Fisher, Jon R. Walsh","submitted_at":"2023-10-27T10:05:06Z","abstract_excerpt":"Effective and rapid decision-making from randomized controlled trials (RCTs) requires unbiased and precise treatment effect inferences. Two strategies to address this requirement are to adjust for covariates that are highly correlated with the outcome, and to leverage historical control information via Bayes' theorem. We propose a new Bayesian prognostic covariate adjustment methodology, referred to as Bayesian PROCOVA, that combines these two strategies. Covariate adjustment in Bayesian PROCOVA is based on generative artificial intelligence (AI) algorithms that construct a digital twin genera"},"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":"2310.18027","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-10-27T10:05:06Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"84e07435158fd0d31fd7a36e201083d02a496eacf29305c285e3123834a720e2","abstract_canon_sha256":"96f78a98d540765cd78f1e01ecc996cc8ab291250d6f035bb7012933f7161188"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:04.395701Z","signature_b64":"cODgBVACbeN/XTnIQYB2XqozVyLVfjKJr+zUHNzrDxYMsFvS1LqzxU9x/CSO8yJ2YeQWmfRb3RfbcVJVrD+oDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f185df4edf1f09fbe35eacd1ab5068cb838f8e5d505b1ec6e1a42a5a54c42f3","last_reissued_at":"2026-07-05T07:50:04.395071Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:04.395071Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.ME","authors_text":"Alyssa M. Vanderbeek, Arman Sabbaghi, Charles K. Fisher, Jon R. Walsh","submitted_at":"2023-10-27T10:05:06Z","abstract_excerpt":"Effective and rapid decision-making from randomized controlled trials (RCTs) requires unbiased and precise treatment effect inferences. Two strategies to address this requirement are to adjust for covariates that are highly correlated with the outcome, and to leverage historical control information via Bayes' theorem. We propose a new Bayesian prognostic covariate adjustment methodology, referred to as Bayesian PROCOVA, that combines these two strategies. Covariate adjustment in Bayesian PROCOVA is based on generative artificial intelligence (AI) algorithms that construct a digital twin genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.18027","kind":"arxiv","version":4},"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/2310.18027/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":"2310.18027","created_at":"2026-07-05T07:50:04.395133+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.18027v4","created_at":"2026-07-05T07:50:04.395133+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.18027","created_at":"2026-07-05T07:50:04.395133+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4MF35HN6HYJ","created_at":"2026-07-05T07:50:04.395133+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4MF35HN6HYJ7PRV","created_at":"2026-07-05T07:50:04.395133+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4MF35HN","created_at":"2026-07-05T07:50:04.395133+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"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":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03282","citing_title":"Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS","json":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS.json","graph_json":"https://pith.science/api/pith-number/F4MF35HN6HYJ7PRV5LGRVNIGRS/graph.json","events_json":"https://pith.science/api/pith-number/F4MF35HN6HYJ7PRV5LGRVNIGRS/events.json","paper":"https://pith.science/paper/F4MF35HN"},"agent_actions":{"view_html":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS","download_json":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS.json","view_paper":"https://pith.science/paper/F4MF35HN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.18027&json=true","fetch_graph":"https://pith.science/api/pith-number/F4MF35HN6HYJ7PRV5LGRVNIGRS/graph.json","fetch_events":"https://pith.science/api/pith-number/F4MF35HN6HYJ7PRV5LGRVNIGRS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS/action/storage_attestation","attest_author":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS/action/author_attestation","sign_citation":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS/action/citation_signature","submit_replication":"https://pith.science/pith/F4MF35HN6HYJ7PRV5LGRVNIGRS/action/replication_record"}},"created_at":"2026-07-05T07:50:04.395133+00:00","updated_at":"2026-07-05T07:50:04.395133+00:00"}