{"doi":"10.1093/jamia/ocaa044","title":"Learning from local to global: An efficient distributed algorithm for modeling time-to-event data","abstract":"OBJECTIVE: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. MATERIALS AND METHODS: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. RESULTS: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. CONCLUSIONS: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.","journal":"Journal of the American Medical Informatics Association","year":2020,"id":57791,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":76,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9484,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":302145,"name":"Chongliang Luo","orcid":"0000-0003-3682-9454","position":1,"is_corresponding":false},{"id":32107,"name":"Martijn J. Schuemie","orcid":"0000-0002-0817-5361","position":2,"is_corresponding":false},{"id":300699,"name":"Jiayi Tong","orcid":"0000-0002-1213-6699","position":3,"is_corresponding":false},{"id":302146,"name":"C Jason Liang","orcid":"0009-0009-7586-144X","position":4,"is_corresponding":false},{"id":239779,"name":"Howard H. Chang","orcid":"0000-0002-6316-1640","position":5,"is_corresponding":false},{"id":76220,"name":"Mary Regina Boland","orcid":"0000-0001-8576-6408","position":6,"is_corresponding":false},{"id":23318,"name":"Jiang Bian","orcid":"0000-0002-2238-5429","position":7,"is_corresponding":false},{"id":12534,"name":"Hua Xu","orcid":"0000-0002-5274-4672","position":8,"is_corresponding":false},{"id":302147,"name":"John H. Holmes","orcid":"0000-0003-2167-3602","position":9,"is_corresponding":false},{"id":302148,"name":"Christopher B. Forrest","orcid":"0000-0003-1252-068X","position":10,"is_corresponding":false},{"id":89367,"name":"Sally C. Morton","orcid":null,"position":11,"is_corresponding":false},{"id":7307,"name":"Jesse A. Berlin","orcid":"0000-0002-9810-745X","position":12,"is_corresponding":false},{"id":14812,"name":"Jason H. Moore","orcid":"0000-0002-5015-1099","position":13,"is_corresponding":false},{"id":302149,"name":"Kevin Mahoney","orcid":"0000-0001-9930-2575","position":14,"is_corresponding":false},{"id":14813,"name":"Yong Chen","orcid":"0000-0003-0835-0788","position":15,"is_corresponding":false},{"id":302144,"name":"Rui Duan","orcid":"0000-0002-9261-4864","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T21:06:48.611399Z","pmid":"32626900","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}