{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:33:21Z","timestamp":1770813201608,"version":"3.50.1"},"reference-count":23,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"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\/100009950","name":"MOE","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009950","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research Institute of Humanities and Social Sciences","award":["22JJD910001"],"award-info":[{"award-number":["22JJD910001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Synthesizing information from multiple data sources is crucial for constructing accurate individualized treatment rules (ITRs). However, privacy concerns often present significant barriers to the integrative analysis of such multicenter data. Classical meta-learning, which averages local estimates to derive the final ITR, is frequently suboptimal due to biases in these local estimates. To address these challenges, we propose a convolution-smoothed weighted support vector machine for learning the optimal ITR. The accompanying loss function is both convex and smooth, which allows us to develop an efficient multiround distributed learning procedure. Such distributed learning ensures optimal statistical performance with a fixed number of communication rounds, thereby minimizing coordination costs across data centers while preserving data privacy. Our method avoids pooling subject-level raw data and instead requires only sharing summary statistics. Additionally, we develop an efficient coordinate gradient descent algorithm, which guarantees at least linear convergence for the resulting optimization problem. Extensive simulations and an application to sepsis treatment across multiple intensive care units validate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag003","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T12:36:36Z","timestamp":1768048596000},"source":"Crossref","is-referenced-by-count":0,"title":["Scalable and distributed individualized treatment rules for multicenter datasets"],"prefix":"10.1093","volume":"82","author":[{"given":"Nan","family":"Qiao","sequence":"first","affiliation":[{"name":"Center for Applied Statistics, Renmin University of China , Beijing 100086 ,","place":["China"]},{"name":"School of Statistics, Renmin University of China , Beijing 100086 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wangcheng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Statistics, Beijing Normal University , Beijing 100086 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1597-3951","authenticated-orcid":false,"given":"Jingxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, Renmin University of China , Beijing 100086 ,","place":["China"]},{"name":"School of Statistics, Renmin University of China , Beijing 100086 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0673-5812","authenticated-orcid":false,"given":"Canyi","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Michigan Department of Biostatistics, , Ann Arbor, MI 48109 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"2026020510362651300_bib1","article-title":"Communication-efficient distributed estimation and inference for Cox\u2019s model","author":"Bayle","year":"2023"},{"key":"2026020510362651300_bib2","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1145\/2897518.2897582","article-title":"Communication lower bounds for statistical estimation problems via a distributed data processing inequality","volume-title":"Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing","author":"Braverman","year":"2016"},{"key":"2026020510362651300_bib3","first-page":"171","article-title":"Meta-analysis of individualized treatment rules via sign-coherency","volume-title":"Machine Learning for Health","author":"Cheng","year":"2022"},{"key":"2026020510362651300_bib4","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Machine Learning"},{"key":"2026020510362651300_bib5","doi-asserted-by":"crossref","first-page":"e1063","DOI":"10.1097\/CCM.0000000000005337","article-title":"Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021","volume":"49","author":"Evans","year":"2021","journal-title":"Critical Care Medicine"},{"key":"2026020510362651300_bib6","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/07350015.2019.1660177","article-title":"Smoothing quantile regressions","volume":"39","author":"Fernandes","year":"2021","journal-title":"Journal of Business and Economic Statistics"},{"key":"2026020510362651300_bib7","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1002\/sim.10068","article-title":"Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies","volume":"43","author":"Hu","year":"2024","journal-title":"Statistics in Medicine"},{"key":"2026020510362651300_bib8","article-title":"Team-Sepsis SQL queries","author":"Jia","year":"2024"},{"key":"2026020510362651300_bib9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Scientific Data"},{"key":"2026020510362651300_bib10","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/01621459.2018.1429274","article-title":"Communication-efficient distributed statistical inference","volume":"114","author":"Jordan","year":"2019","journal-title":"Journal of the American Statistical Association"},{"key":"2026020510362651300_bib11","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1111\/biom.12191","article-title":"Combining biomarkers to optimize patient treatment recommendations","volume":"70","author":"Kang","year":"2014","journal-title":"Biometrics"},{"key":"2026020510362651300_bib12","first-page":"37","article-title":"Early lactate levels for prediction of mortality in patients with sepsis or septic shock: a meta-analysis","volume":"10","author":"Liu","year":"2017","journal-title":"International Journal of Clinical and Experimental Medicine"},{"key":"2026020510362651300_bib13","doi-asserted-by":"crossref","first-page":"3776","DOI":"10.1002\/sim.7844","article-title":"Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens","volume":"37","author":"Liu","year":"2018","journal-title":"Statistics in Medicine"},{"key":"2026020510362651300_bib14","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/01621459.2020.1725522","article-title":"Improved doubly robust estimation in learning optimal individualized treatment rules","volume":"116","author":"Pan","year":"2021","journal-title":"Journal of the American Statistical Association"},{"key":"2026020510362651300_bib15","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1001\/jama.2017.13836","article-title":"Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014","volume":"318","author":"Rhee","year":"2017","journal-title":"Journal of the American Medical Association"},{"key":"2026020510362651300_bib16","first-page":"1","article-title":"Communication-constrained distributed quantile regression with optimal statistical guarantees","volume":"23","author":"Tan","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"2026020510362651300_bib17","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1080\/10618600.2022.2141752","article-title":"Transfer learning of individualized treatment rules from experimental to real-world data","volume":"32","author":"Wu","year":"2023","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"2026020510362651300_bib18","article-title":"Fedmood: Federated learning on mobile health data for mood detection","author":"Xu","year":"2021"},{"key":"2026020510362651300_bib19","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1002\/sta.411","article-title":"Estimating optimal treatment regimes from a classification perspective","volume":"1","author":"Zhang","year":"2012","journal-title":"Stat"},{"key":"2026020510362651300_bib20","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1111\/biom.12836","article-title":"C-learning: A new classification framework to estimate optimal dynamic treatment regimes","volume":"74","author":"Zhang","year":"2018","journal-title":"Biometrics."},{"key":"2026020510362651300_bib21","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1080\/01621459.2012.695674","article-title":"Estimating individualized treatment rules using outcome weighted learning","volume":"107","author":"Zhao","year":"2012","journal-title":"Journal of the American Statistical Association"},{"key":"2026020510362651300_bib22","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1093\/biomet\/asu050","article-title":"Doubly robust learning for estimating individualized treatment with censored data","volume":"102","author":"Zhao","year":"2015","journal-title":"Biometrika"},{"key":"2026020510362651300_bib23","doi-asserted-by":"crossref","first-page":"3152","DOI":"10.1080\/01621459.2024.2310287","article-title":"Federated offline reinforcement learning","volume":"119","author":"Zhou","year":"2024","journal-title":"Journal of the American Statistical Association"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/82\/1\/ujag003\/66788197\/ujag003.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/82\/1\/ujag003\/66788197\/ujag003.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T15:36:36Z","timestamp":1770305796000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/doi\/10.1093\/biomtc\/ujag003\/8465356"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,6]]}},"URL":"https:\/\/doi.org\/10.1093\/biomtc\/ujag003","relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"value":"0006-341X","type":"print"},{"value":"1541-0420","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,3]]},"published":{"date-parts":[[2026,1,6]]},"article-number":"ujag003"}}