Literature DB >> 29554470

The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

Oguzhan Alagoz1, Mehmet Ali Ergun1, Mucahit Cevik2, Brian L Sprague3, Dennis G Fryback4, Ronald E Gangnon5, John M Hampton6, Natasha K Stout7, Amy Trentham-Dietz6.   

Abstract

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's 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.

Entities:  

Keywords:  breast cancer; incidence; screening; simulation

Mesh:

Year:  2018        PMID: 29554470      PMCID: PMC5862066          DOI: 10.1177/0272989X17711927

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  31 in total

Review 1.  Breast cancer (3).

Authors:  J R Harris; M E Lippman; U Veronesi; W Willett
Journal:  N Engl J Med       Date:  1992-08-13       Impact factor: 91.245

Review 2.  Breast cancer (1)

Authors:  J R Harris; M E Lippman; U Veronesi; W Willett
Journal:  N Engl J Med       Date:  1992-07-30       Impact factor: 91.245

3.  Bias associated with self-report of prior screening mammography.

Authors:  Kathleen A Cronin; Diana L Miglioretti; Martin Krapcho; Binbing Yu; Berta M Geller; Patricia A Carney; Tracy Onega; Eric J Feuer; Nancy Breen; Rachel Ballard-Barbash
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-06       Impact factor: 4.254

4.  Variation in tumor natural history contributes to racial disparities in breast cancer stage at diagnosis.

Authors:  Nataliya G Batina; Amy Trentham-Dietz; Ronald E Gangnon; Brian L Sprague; Marjorie A Rosenberg; Natasha K Stout; Dennis G Fryback; Oguzhan Alagoz
Journal:  Breast Cancer Res Treat       Date:  2013-02-16       Impact factor: 4.872

5.  Using Active Learning for Speeding up Calibration in Simulation Models.

Authors:  Mucahit Cevik; Mehmet Ali Ergun; Natasha K Stout; Amy Trentham-Dietz; Mark Craven; Oguzhan Alagoz
Journal:  Med Decis Making       Date:  2015-10-15       Impact factor: 2.583

6.  Comparative effectiveness of combined digital mammography and tomosynthesis screening for women with dense breasts.

Authors:  Christoph I Lee; Mucahit Cevik; Oguzhan Alagoz; Brian L Sprague; Anna N A Tosteson; Diana L Miglioretti; Karla Kerlikowske; Natasha K Stout; Jeffrey G Jarvik; Scott D Ramsey; Constance D Lehman
Journal:  Radiology       Date:  2014-10-28       Impact factor: 11.105

7.  Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts.

Authors:  Brian L Sprague; Natasha K Stout; Clyde Schechter; Nicolien T van Ravesteyn; Mucahit Cevik; Oguzhan Alagoz; Christoph I Lee; Jeroen J van den Broek; Diana L Miglioretti; Jeanne S Mandelblatt; Harry J de Koning; Karla Kerlikowske; Constance D Lehman; Anna N A Tosteson
Journal:  Ann Intern Med       Date:  2015-02-03       Impact factor: 25.391

8.  A sustained decline in postmenopausal hormone use: results from the National Health and Nutrition Examination Survey, 1999-2010.

Authors:  Brian L Sprague; Amy Trentham-Dietz; Kathleen A Cronin
Journal:  Obstet Gynecol       Date:  2012-09       Impact factor: 7.661

9.  Prevalence of mammographically dense breasts in the United States.

Authors:  Brian L Sprague; Ronald E Gangnon; Veronica Burt; Amy Trentham-Dietz; John M Hampton; Robert D Wellman; Karla Kerlikowske; Diana L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2014-09-12       Impact factor: 13.506

10.  Trends of postmenopausal estrogen plus progestin prevalence in the United States between 1970 and 2010.

Authors:  Patricia I Jewett; Ronald E Gangnon; Amy Trentham-Dietz; Brian L Sprague
Journal:  Obstet Gynecol       Date:  2014-10       Impact factor: 7.661

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  16 in total

1.  Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012.

Authors:  Sylvia K Plevritis; Diego Munoz; Allison W Kurian; Natasha K Stout; Oguzhan Alagoz; Aimee M Near; Sandra J Lee; Jeroen J van den Broek; Xuelin Huang; Clyde B Schechter; Brian L Sprague; Juhee Song; Harry J de Koning; Amy Trentham-Dietz; Nicolien T van Ravesteyn; Ronald Gangnon; Young Chandler; Yisheng Li; Cong Xu; Mehmet Ali Ergun; Hui Huang; Donald A Berry; Jeanne S Mandelblatt
Journal:  JAMA       Date:  2018-01-09       Impact factor: 56.272

2.  Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening.

Authors:  Amy Trentham-Dietz; Mehmet Ali Ergun; Oguzhan Alagoz; Natasha K Stout; Ronald E Gangnon; John M Hampton; Kim Dittus; Ted A James; Pamela M Vacek; Sally D Herschorn; Elizabeth S Burnside; Anna N A Tosteson; Donald L Weaver; Brian L Sprague
Journal:  Breast Cancer Res Treat       Date:  2017-11-28       Impact factor: 4.872

3.  Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated With Chest Radiation : A Comparative Modeling Study.

Authors:  Jennifer M Yeh; Kathryn P Lowry; Clyde B Schechter; Lisa R Diller; Oguzhan Alagoz; Gregory T Armstrong; John M Hampton; Wendy Leisenring; Qi Liu; Jeanne S Mandelblatt; Diana L Miglioretti; Chaya S Moskowitz; Kevin C Oeffinger; Amy Trentham-Dietz; Natasha K Stout
Journal:  Ann Intern Med       Date:  2020-07-07       Impact factor: 25.391

4.  Cost-effectiveness of mammography from a publicly funded health care system perspective.

Authors:  Nicole Mittmann; Natasha K Stout; Anna N A Tosteson; Amy Trentham-Dietz; Oguzhan Alagoz; Martin J Yaffe
Journal:  CMAJ Open       Date:  2018-02-08

5.  Representing Tuberculosis Transmission with Complex Contagion: An Agent-Based Simulation Modeling Approach.

Authors:  Erin D Zwick; Caitlin S Pepperell; Oguzhan Alagoz
Journal:  Med Decis Making       Date:  2021-04-27       Impact factor: 2.583

6.  Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis.

Authors:  Kathryn P Lowry; H Amarens Geuzinge; Natasha K Stout; Oguzhan Alagoz; John Hampton; Karla Kerlikowske; Harry J de Koning; Diana L Miglioretti; Nicolien T van Ravesteyn; Clyde Schechter; Brian L Sprague; Anna N A Tosteson; Amy Trentham-Dietz; Donald Weaver; Martin J Yaffe; Jennifer M Yeh; Fergus J Couch; Chunling Hu; Peter Kraft; Eric C Polley; Jeanne S Mandelblatt; Allison W Kurian; Mark E Robson
Journal:  JAMA Oncol       Date:  2022-04-01       Impact factor: 33.006

7.  Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening With Digital Breast Tomosynthesis in the United States.

Authors:  Kathryn P Lowry; Amy Trentham-Dietz; Clyde B Schechter; Oguzhan Alagoz; William E Barlow; Elizabeth S Burnside; Emily F Conant; John M Hampton; Hui Huang; Karla Kerlikowske; Sandra J Lee; Diana L Miglioretti; Brian L Sprague; Anna N A Tosteson; Martin J Yaffe; Natasha K Stout
Journal:  J Natl Cancer Inst       Date:  2020-06-01       Impact factor: 13.506

8.  Benefits and Harms of Mammography Screening for Women With Down Syndrome: a Collaborative Modeling Study.

Authors:  Oguzhan Alagoz; Ali Hajjar; Sarocha Chootipongchaivat; Nicolien T van Ravesteyn; Jennifer M Yeh; Mehmet Ali Ergun; Harry J de Koning; Brian Chicoine; Barry Martin
Journal:  J Gen Intern Med       Date:  2019-08-05       Impact factor: 5.128

Review 9.  Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.

Authors:  Amy Trentham-Dietz; Oguzhan Alagoz; Christina Chapman; Xuelin Huang; Jinani Jayasekera; Nicolien T van Ravesteyn; Sandra J Lee; Clyde B Schechter; Jennifer M Yeh; Sylvia K Plevritis; Jeanne S Mandelblatt
Journal:  PLoS Comput Biol       Date:  2021-06-17       Impact factor: 4.475

10.  Breast Cancer Screening Among Childhood Cancer Survivors Treated Without Chest Radiation: Clinical Benefits and Cost-Effectiveness.

Authors:  Jennifer M Yeh; Kathryn P Lowry; Clyde B Schechter; Lisa R Diller; Grace O'Brien; Oguzhan Alagoz; Gregory T Armstrong; John M Hampton; Melissa M Hudson; Wendy Leisenring; Qi Liu; Jeanne S Mandelblatt; Diana L Miglioretti; Chaya S Moskowitz; Paul C Nathan; Joseph P Neglia; Kevin C Oeffinger; Amy Trentham-Dietz; Natasha K Stout
Journal:  J Natl Cancer Inst       Date:  2022-02-07       Impact factor: 11.816

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