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We proposed a One-shot Distributed Algorithm to fit multi-center Cox regression models with Time varying coefficients (ODACT). This algorithm constructed a surrogate likelihood function to approximate the Cox partial likelihood function, using patient-level data from a lead site and aggregated data from other sites. The performance of ODACT was demonstrated by simulation and a real-world study of opioid use disorder (OUD) using decentralized data from a large clinical research network across 5 sites with 69\u00a0163 subjects.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The ODACT method precisely estimated the time-varying effects over time. In the simulation study, ODACT always achieved estimation close to that of the pooled analysis, while the meta-estimator showed considerable amount of bias. In the OUD study, the bias of the estimated hazard ratios by ODACT are smaller than those of the meta-estimator for all 7 risk factors at almost all of the time points from 0 to 2.5 years. The greatest bias of the meta-estimator was for the effects of age\u2009\u226565 years, and smoking.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>ODACT is a privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data which allows the covariates\u2019 effects to be time-varying. ODACT provides estimates close to the pooled estimator and substantially outperforms the meta-analysis estimator.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The proposed ODACT is a privacy-preserving distributed algorithm for fitting Cox models with time-varying coefficients. The limitations of ODACT include that privacy-preserving via aggregate data does rely on relatively large number of data at each individual site, and rigorous quantification of the risk of privacy leaks requires further investigation.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae313","type":"journal-article","created":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T23:05:46Z","timestamp":1737932746000},"page":"656-664","source":"Crossref","is-referenced-by-count":2,"title":["Communication-efficient federated learning of temporal effects on opioid use disorder with data from distributed research networks"],"prefix":"10.1093","volume":"32","author":[{"given":"C Jason","family":"Liang","sequence":"first","affiliation":[{"name":"Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases , Bethesda, MD 20892,","place":["United 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