Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling

Jeanne S. Mandelblatt, Aimee M. Near, Diana L. Miglioretti, Diego Munoz, Brian L. Sprague, Amy Trentham-Dietz, Ronald Gangnon, Allison W. Kurian, Harald Weedon-Fekjaer, Kathleen A. Cronin and Sylvia K. Plevritis
Additional contact information
Jeanne S. Mandelblatt: Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
Aimee M. Near: Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
Diana L. Miglioretti: Department of Public Health Sciences, UC Davis School of Medicine, Davis, California, USA and Group Health Research Institute, Seattle, WA, USA and Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
Diego Munoz: Departments of Biomedical Informatics and Radiology, School of Medicine, Stanford University, Stanford, California, USA
Brian L. Sprague: Department of Surgery, College of Medicine, University of Vermont, Burlington, Vermont, USA
Amy Trentham-Dietz: Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
Ronald Gangnon: Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
Allison W. Kurian: Departments of Medicine and Health Research & Policy, School of Medicine, Stanford University, Stanford, California, USA
Harald Weedon-Fekjaer: Oslo Center for Biostatistics and Epidemiology [OCBE], Research Support Services, Oslo University Hospital, Oslo, Norway
Kathleen A. Cronin: Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
Sylvia K. Plevritis: Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 9S-23S

Abstract: Background. Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality. Method and Results. In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters. Conclusion. These data are intended to enhance the transparency of the breast CISNET models.

Keywords: breast cancer epidemiology; cancer simulation; simulation models (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:9s-23s

DOI: 10.1177/0272989X17700624

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