{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T04:01:02Z","timestamp":1773288062276,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"National Institute of Allergy and Infectious Diseases of the National Institutes of Health","award":["R01 AI136947"],"award-info":[{"award-number":["R01 AI136947"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                  <jats:p>Estimating parameters corresponding to mean outcomes and their intricate association structures in cluster randomized trials (CRTs) can pose significant methodological challenges. This paper introduces a novel framework that leverages network concepts to represent complex dependency structures and estimate these parameters using generalized estimating equations (GEE). We focus on modeling complex correlation structures by partitioning observations into potentially overlapping groups of interrelated data, where observations are assumed locally exchangeable within each group. This network GEE framework is inherently flexible, and we demonstrate its application to multiple exchangeable structures (simple, nested, block), moving average structures, and exponential decay structures. Furthermore, to address computational challenges arising in GEEs with large cluster sizes, we present the networkGEE R package, enabling the fitting of models beyond the capabilities of existing statistical software. The proposed methods are evaluated through extensive simulation studies. To illustrate their practical application, we analyze data from the Washington State Expedited Partners Therapy trial, a stepped-wedge CRT designed to assess the impact of a public health intervention aimed at reducing sexually transmitted infections through free patient-delivered partner therapy.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxaf039","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:18:00Z","timestamp":1760012280000},"source":"Crossref","is-referenced-by-count":0,"title":["Network generalized estimating equations for complexly correlated data with applications to cluster randomized trials"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5301-5447","authenticated-orcid":false,"given":"Tom","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , 401 Park Drive, Suite 401 East , Boston, Massachusetts 02215,","place":["United States"]},{"name":"Department of Biostatistics, Harvard T. H. Chan School of Public Health , 655 Huntington Avenue Building 2, 4th Floor , Boston, Massachusetts 02115,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6183-1893","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health , 60 College St , New Haven, Connecticut 06510,","place":["United States"]},{"name":"Department of Internal Medicine , 333 Cedar Street , New Haven, Connecticut 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5007-193X","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , 401 Park Drive, Suite 401 East , Boston, Massachusetts 02215,","place":["United States"]},{"name":"Department of Biostatistics, Harvard T. 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Chan School of Public Health , 655 Huntington Avenue Building 2, 4th Floor , Boston, Massachusetts 02115,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"2026031108185495000_kxaf039-B1","first-page":"177","author":"Bottou","year":"2010"},{"key":"2026031108185495000_kxaf039-B2","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1137\/140954362","article-title":"A stochastic quasi-newton method for large-scale optimization","volume":"26","author":"Byrd","year":"2016","journal-title":"SIAM J Optim"},{"key":"2026031108185495000_kxaf039-B3","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1093\/biomet\/80.3.517","article-title":"Modelling multivariate binary data with alternating logistic 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