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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 &amp;lt;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 &amp;lt;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 &amp;lt;5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa044","type":"journal-article","created":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T08:09:42Z","timestamp":1585382982000},"page":"1028-1036","source":"Crossref","is-referenced-by-count":70,"title":["Learning from local to global: An efficient distributed algorithm for modeling time-to-event data"],"prefix":"10.1093","volume":"27","author":[{"given":"Rui","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA"}]},{"given":"Chongliang","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, 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