{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:S3VI7LRS6GQ4RGUY4L5ZUL34DO","short_pith_number":"pith:S3VI7LRS","schema_version":"1.0","canonical_sha256":"96ea8fae32f1a1c89a98e2fb9a2f7c1bb3d3b48875f3c1816f1179b1434dff2c","source":{"kind":"arxiv","id":"1703.10266","version":2},"attestation_state":"computed","paper":{"title":"Bayesian latent time joint mixed effect models for multicohort longitudinal data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.AP","authors_text":"Dan Li, Michael C. Donohue, Samuel Iddi, Wesley K. Thompson","submitted_at":"2017-03-29T23:19:34Z","abstract_excerpt":"Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference"},"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":"1703.10266","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2017-03-29T23:19:34Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"b6c5fae89e00cfe0ec277ea1b5015d861adace1e7f1ae7c4c382e150997ef003","abstract_canon_sha256":"03f7fbbbbbf7649638accf5df01f233b82fbf106f0fc86a97c1f424303799525"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:16.476003Z","signature_b64":"qXt9jNFeHsvulv2RN7kwhwfHYcxK5ysJU4jE7e5cp6z0t8g61g5BBbwvwxQmY4f9KiI9kPv4/z9tF4VsfMQGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96ea8fae32f1a1c89a98e2fb9a2f7c1bb3d3b48875f3c1816f1179b1434dff2c","last_reissued_at":"2026-05-18T00:26:16.475417Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:16.475417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian latent time joint mixed effect models for multicohort longitudinal data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.AP","authors_text":"Dan Li, Michael C. Donohue, Samuel Iddi, Wesley K. Thompson","submitted_at":"2017-03-29T23:19:34Z","abstract_excerpt":"Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.10266","kind":"arxiv","version":2},"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":"1703.10266","created_at":"2026-05-18T00:26:16.475527+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.10266v2","created_at":"2026-05-18T00:26:16.475527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.10266","created_at":"2026-05-18T00:26:16.475527+00:00"},{"alias_kind":"pith_short_12","alias_value":"S3VI7LRS6GQ4","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"S3VI7LRS6GQ4RGUY","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"S3VI7LRS","created_at":"2026-05-18T12:31:43.269735+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/S3VI7LRS6GQ4RGUY4L5ZUL34DO","json":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO.json","graph_json":"https://pith.science/api/pith-number/S3VI7LRS6GQ4RGUY4L5ZUL34DO/graph.json","events_json":"https://pith.science/api/pith-number/S3VI7LRS6GQ4RGUY4L5ZUL34DO/events.json","paper":"https://pith.science/paper/S3VI7LRS"},"agent_actions":{"view_html":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO","download_json":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO.json","view_paper":"https://pith.science/paper/S3VI7LRS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.10266&json=true","fetch_graph":"https://pith.science/api/pith-number/S3VI7LRS6GQ4RGUY4L5ZUL34DO/graph.json","fetch_events":"https://pith.science/api/pith-number/S3VI7LRS6GQ4RGUY4L5ZUL34DO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO/action/storage_attestation","attest_author":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO/action/author_attestation","sign_citation":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO/action/citation_signature","submit_replication":"https://pith.science/pith/S3VI7LRS6GQ4RGUY4L5ZUL34DO/action/replication_record"}},"created_at":"2026-05-18T00:26:16.475527+00:00","updated_at":"2026-05-18T00:26:16.475527+00:00"}