{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:15:45Z","timestamp":1776381345775,"version":"3.51.2"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01LM012607"],"award-info":[{"award-number":["1R01LM012607"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01AI130460"],"award-info":[{"award-number":["1R01AI130460"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P50MH113840"],"award-info":[{"award-number":["P50MH113840"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01AI116794"],"award-info":[{"award-number":["1R01AI116794"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01LM009012"],"award-info":[{"award-number":["R01LM009012"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was &amp;lt;3%, and the ratio of standard errors was &amp;lt;1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias &amp;lt;0.1% and ratio of standard errors &amp;lt;1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias &amp;lt;10% for 85% of medications, and ODAL2 has relative bias &amp;lt;10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p\u00d7p+p) numbers from each site to the local site, where p is the number of parameters in the regression model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocz199","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T19:12:02Z","timestamp":1571857922000},"page":"376-385","source":"Crossref","is-referenced-by-count":97,"title":["Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9261-4864","authenticated-orcid":false,"given":"Rui","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8576-6408","authenticated-orcid":false,"given":"Mary Regina","family":"Boland","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}]},{"given":"Zixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Stanford University, Stanford, California, USA"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Harvard University, Cambridge, Massachusetts, USA"}]},{"given":"Howard H","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA"}]},{"given":"Hua","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA"}]},{"given":"Haitao","family":"Chu","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA"}]},{"given":"Christopher H","family":"Schmid","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Brown University, Providence, Rhode Island, USA"}]},{"given":"Christopher B","family":"Forrest","sequence":"additional","affiliation":[{"name":"Division of General Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA"}]},{"given":"John H","family":"Holmes","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0817-5361","authenticated-orcid":false,"given":"Martijn J","family":"Schuemie","sequence":"additional","affiliation":[{"name":"Janssen Research and Development LLC, Titusville, New Jersey, USA"}]},{"given":"Jesse A","family":"Berlin","sequence":"additional","affiliation":[{"name":"Janssen Research and Development LLC, Titusville, New Jersey, USA"}]},{"given":"Jason H","family":"Moore","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0835-0788","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and 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