Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling

Author:

Mandelblatt Jeanne S.1,Near Aimee M.1,Miglioretti Diana L.2,Munoz Diego3,Sprague Brian L.4,Trentham-Dietz Amy5,Gangnon Ronald56,Kurian Allison W.7,Weedon-Fekjaer Harald8,Cronin Kathleen A.9,Plevritis Sylvia K.10

Affiliation:

1. Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA

2. 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

3. Departments of Biomedical Informatics and Radiology, School of Medicine, Stanford University, Stanford, California, USA

4. Department of Surgery, College of Medicine, University of Vermont, Burlington, Vermont, USA

5. Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA

6. Department of Biostatistics and Medical Informatics and Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA

7. Departments of Medicine and Health Research & Policy, School of Medicine, Stanford University, Stanford, California, USA

8. Oslo Center for Biostatistics and Epidemiology [OCBE], Research Support Services, Oslo University Hospital, Oslo, Norway

9. Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA

10. Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA

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.

Funder

National Cancer Institute

Publisher

SAGE Publications

Subject

Health Policy

Cited by 36 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3