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Showing content from https://pubmed.ncbi.nlm.nih.gov/29554472/ below:

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

. 2018 Apr;38(1_suppl):3S-8S. doi: 10.1177/0272989X17737507. Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

Affiliations

¹ Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
² Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
³ Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
⁴ Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA.
⁵ 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.
⁶ Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
⁷ Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
⁸ Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
⁹ Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA.
¹⁰ Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

PMID: 29554472
PMCID: PMC5862043
DOI: 10.1177/0272989X17737507

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Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

Oguzhan Alagoz et al. Med Decis Making. 2018 Apr.

. 2018 Apr;38(1_suppl):3S-8S. doi: 10.1177/0272989X17737507. Affiliations

¹ Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
² Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA.
³ Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
⁴ Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA.
⁵ 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.
⁶ Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
⁷ Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
⁸ Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
⁹ Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA.
¹⁰ Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

PMID: 29554472
PMCID: PMC5862043
DOI: 10.1177/0272989X17737507

Item in Clipboard

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.

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