@article{9d40913fdad9410b9960ece8afd357d2,
title = "Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models",
abstract = "The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute–sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.",
keywords = "breast cancer control, breast cancer epidemiology, cancer simulation, simulation models",
author = "{on behalf of CISNET Breast Cancer Working Group members} and Oguzhan Alagoz and Berry, {Donald A.} and {de Koning}, {Harry J.} and Feuer, {Eric J.} and Lee, {Sandra J.} and Plevritis, {Sylvia K.} and Schechter, {Clyde B.} and Stout, {Natasha K.} and Amy Trentham-Dietz and Mandelblatt, {Jeanne S.}",
note = "Funding Information: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA (OA); Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA (DAB); Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA (EJF); Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands (HJd); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA (SJL); Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA (SP); Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA (CBS); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA (NKS); Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison, WI, USA (AT); and Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA (JSM). *This work was done by 6 independent modeling teams from Dana-Farber Cancer Institute (principal investigator [PI]: Lee), Erasmus Medical Center (PI: de Koning), Georgetown University Medical Center, Lombardi Comprehensive Cancer Center/Albert Einstein College of Medicine (PI: Mandelblatt/Schechter), Harvard Medical School, University of Wisconsin/Harvard Pilgrim Health Care (PI: Trentham-Dietz/Stout/Alagoz), M. D. Anderson Comprehensive Cancer Center (PI: Berry), and Stanford University (PI: Plevritis). Jeanne Mandelblatt was the senior author and Eric Feuer was responsible for overall CISNET project direction. This work was supported by the National Institutes of Health under National Cancer Institute grants U01CA152958, U01CA199218, U01CA088278, U01CA088211, U01CA088202, U01CA088283, U01CA088248, U01CA088270, U01CA088177, U01CA88293A, and U01CA116532. Publisher Copyright: {\textcopyright} 2017, {\textcopyright} The Author(s) 2017.",
year = "2018",
month = apr,
day = "1",
doi = "10.1177/0272989X17737507",
language = "English (US)",
volume = "38",
pages = "3S--8S",
journal = "Medical Decision Making",
issn = "0272-989X",
publisher = "SAGE Publications Inc.",
number = "1_suppl",
}
