{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:47:44Z","timestamp":1777916864214,"version":"3.51.4"},"reference-count":44,"publisher":"SAGE Publications","issue":"1_suppl","license":[{"start":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T00:00:00Z","timestamp":1521417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA152958, U01CA19921"],"award-info":[{"award-number":["U01CA152958, U01CA19921"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2018,4]]},"abstract":"<jats:p>The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman\u2019s lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER\/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.<\/jats:p>","DOI":"10.1177\/0272989x17711927","type":"journal-article","created":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T18:40:23Z","timestamp":1521484823000},"page":"99S-111S","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":49,"title":["The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update"],"prefix":"10.1177","volume":"38","author":[{"given":"Oguzhan","family":"Alagoz","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehmet Ali","family":"Ergun","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mucahit","family":"Cevik","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian L.","family":"Sprague","sequence":"additional","affiliation":[{"name":"Department of Surgery and University of Vermont Cancer Center, University of Vermont, Burlington, VT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dennis G.","family":"Fryback","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald E.","family":"Gangnon","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John M.","family":"Hampton","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasha K.","family":"Stout","sequence":"additional","affiliation":[{"name":"Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amy","family":"Trentham-Dietz","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,19]]},"reference":[{"key":"bibr1-0272989X17711927","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa050518"},{"key":"bibr2-0272989X17711927","doi-asserted-by":"publisher","DOI":"10.1093\/jncimonographs\/lgj007"},{"key":"bibr3-0272989X17711927","unstructured":"Cancer Intervention and Surveillance Modeling Network (CISNET). 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