{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6DMYL2ZGDKC5B5AIVFY2PGBHPB","short_pith_number":"pith:6DMYL2ZG","schema_version":"1.0","canonical_sha256":"f0d985eb261a85d0f408a971a798277853731a03ba7ac533b0ebae83e8db6a1e","source":{"kind":"arxiv","id":"1801.01013","version":1},"attestation_state":"computed","paper":{"title":"Instrumental variables estimation with competing risk data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Stijn Vansteelandt, Torben Martinussen","submitted_at":"2017-12-30T13:18:23Z","abstract_excerpt":"Time-to-event analyses are often plagued by both -- possibly unmeasured -- confounding and competing risks. To deal with the former, the use of instrumental variables for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and instrumental variables of arbitrary ty"},"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":"1801.01013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-12-30T13:18:23Z","cross_cats_sorted":[],"title_canon_sha256":"b1e8fe2f8e704c7710af0ac6855cc9767c2189fe654c31158af054025a03ed92","abstract_canon_sha256":"90ba9feb3580e3199dd9286460aa90fedf600df95c70360bbea97a4f7be61b50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:48.086961Z","signature_b64":"5lfbDLDRE85+/X4wq+pXki6lrBwO4CazEIDZ7J26Hvj6+S49iEIgRInrJGxEr7kOqDslvb4172eXIjZPEqiXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0d985eb261a85d0f408a971a798277853731a03ba7ac533b0ebae83e8db6a1e","last_reissued_at":"2026-05-18T00:26:48.086207Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:48.086207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instrumental variables estimation with competing risk data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Stijn Vansteelandt, Torben Martinussen","submitted_at":"2017-12-30T13:18:23Z","abstract_excerpt":"Time-to-event analyses are often plagued by both -- possibly unmeasured -- confounding and competing risks. To deal with the former, the use of instrumental variables for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and instrumental variables of arbitrary ty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.01013","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":""},"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":"1801.01013","created_at":"2026-05-18T00:26:48.086343+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.01013v1","created_at":"2026-05-18T00:26:48.086343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.01013","created_at":"2026-05-18T00:26:48.086343+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DMYL2ZGDKC5","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DMYL2ZGDKC5B5AI","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DMYL2ZG","created_at":"2026-05-18T12:31:03.183658+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/6DMYL2ZGDKC5B5AIVFY2PGBHPB","json":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB.json","graph_json":"https://pith.science/api/pith-number/6DMYL2ZGDKC5B5AIVFY2PGBHPB/graph.json","events_json":"https://pith.science/api/pith-number/6DMYL2ZGDKC5B5AIVFY2PGBHPB/events.json","paper":"https://pith.science/paper/6DMYL2ZG"},"agent_actions":{"view_html":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB","download_json":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB.json","view_paper":"https://pith.science/paper/6DMYL2ZG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.01013&json=true","fetch_graph":"https://pith.science/api/pith-number/6DMYL2ZGDKC5B5AIVFY2PGBHPB/graph.json","fetch_events":"https://pith.science/api/pith-number/6DMYL2ZGDKC5B5AIVFY2PGBHPB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB/action/storage_attestation","attest_author":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB/action/author_attestation","sign_citation":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB/action/citation_signature","submit_replication":"https://pith.science/pith/6DMYL2ZGDKC5B5AIVFY2PGBHPB/action/replication_record"}},"created_at":"2026-05-18T00:26:48.086343+00:00","updated_at":"2026-05-18T00:26:48.086343+00:00"}