{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LWU6V4DZV7FX2RNW4LUM7N5W3P","short_pith_number":"pith:LWU6V4DZ","schema_version":"1.0","canonical_sha256":"5da9eaf079afcb7d45b6e2e8cfb7b6dbf9e0fdaeb79c793532210471e2403989","source":{"kind":"arxiv","id":"2505.15944","version":1},"attestation_state":"computed","paper":{"title":"Optimal Treatment Allocations Accounting for Population Differences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Aiyi Liu, Wei Zhang, Zhiwei Zhang","submitted_at":"2025-05-21T19:00:46Z","abstract_excerpt":"The treatment allocation mechanism in a randomized clinical trial can be optimized by maximizing the nonparametric efficiency bound for a specific measure of treatment effect. Optimal treatment allocations which may or may not depend on baseline covariates have been derived for a variety of effect measures focusing on the trial population, the patient population represented by the trial participants. Frequently, clinical trial data are used to estimate treatment effects in a target population that is related to but different from the trial population. This article provides optimal treatment al"},"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":"2505.15944","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-05-21T19:00:46Z","cross_cats_sorted":[],"title_canon_sha256":"bf8b00057a7feec8526720fbe7fde1d89eda0ef6e9a643061184a03b8b00d478","abstract_canon_sha256":"44e5953acbabbd8d9feee55e1854b9b0ad3d1cf223f370d87ee5d5173f34ca78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:27.647818Z","signature_b64":"puyZ0d9BV66BKBlHOD6XWbXERF6tuhjijiJm3HF8PV6JRmHqLXCy9nnRchA7JUAsnPZpvK8ACpksEfzxqJXjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5da9eaf079afcb7d45b6e2e8cfb7b6dbf9e0fdaeb79c793532210471e2403989","last_reissued_at":"2026-07-05T11:07:27.647336Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:27.647336Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Treatment Allocations Accounting for Population Differences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Aiyi Liu, Wei Zhang, Zhiwei Zhang","submitted_at":"2025-05-21T19:00:46Z","abstract_excerpt":"The treatment allocation mechanism in a randomized clinical trial can be optimized by maximizing the nonparametric efficiency bound for a specific measure of treatment effect. Optimal treatment allocations which may or may not depend on baseline covariates have been derived for a variety of effect measures focusing on the trial population, the patient population represented by the trial participants. Frequently, clinical trial data are used to estimate treatment effects in a target population that is related to but different from the trial population. This article provides optimal treatment al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15944","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/2505.15944/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":"2505.15944","created_at":"2026-07-05T11:07:27.647391+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.15944v1","created_at":"2026-07-05T11:07:27.647391+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15944","created_at":"2026-07-05T11:07:27.647391+00:00"},{"alias_kind":"pith_short_12","alias_value":"LWU6V4DZV7FX","created_at":"2026-07-05T11:07:27.647391+00:00"},{"alias_kind":"pith_short_16","alias_value":"LWU6V4DZV7FX2RNW","created_at":"2026-07-05T11:07:27.647391+00:00"},{"alias_kind":"pith_short_8","alias_value":"LWU6V4DZ","created_at":"2026-07-05T11:07:27.647391+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06512","citing_title":"Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift","ref_index":2016,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P","json":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P.json","graph_json":"https://pith.science/api/pith-number/LWU6V4DZV7FX2RNW4LUM7N5W3P/graph.json","events_json":"https://pith.science/api/pith-number/LWU6V4DZV7FX2RNW4LUM7N5W3P/events.json","paper":"https://pith.science/paper/LWU6V4DZ"},"agent_actions":{"view_html":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P","download_json":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P.json","view_paper":"https://pith.science/paper/LWU6V4DZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.15944&json=true","fetch_graph":"https://pith.science/api/pith-number/LWU6V4DZV7FX2RNW4LUM7N5W3P/graph.json","fetch_events":"https://pith.science/api/pith-number/LWU6V4DZV7FX2RNW4LUM7N5W3P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P/action/storage_attestation","attest_author":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P/action/author_attestation","sign_citation":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P/action/citation_signature","submit_replication":"https://pith.science/pith/LWU6V4DZV7FX2RNW4LUM7N5W3P/action/replication_record"}},"created_at":"2026-07-05T11:07:27.647391+00:00","updated_at":"2026-07-05T11:07:27.647391+00:00"}