Breast cancer remains the second leading cause of cancer morbidity and mortality among women in the US. New discoveries have resulted in the widely accepted view that breast cancer is a heterogeneous disease with molecularly distinguishable morphological subtypes. This awareness is driving the development of new paradigms for the prevention, early detection and clinical management of breast cancer. However, there are very limited data on the population effects of these novel cancer control approaches. Population modeling is a unique comparative effectiveness paradigm to fill this gap by translating advances from the laboratory and clinical trials to understanding their net effects on US breast cancer mortality. The CISNET Breast Working Group has collaborated over the past nine years to apply independent population models to evaluate cancer control practices and use results to inform clinical and public health guidelines. This proposal leverages the investment in these models and provides the continuity and cohesion of this highly productive group. The modeling groups include Dana Farber (D). Erasmus MC (E), Georgetown-Einstein (G), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). For this application, we will extend our work by modeling populations of women with varying risk factors (e.g., breast density, HRT) for the development of specific molecular subtypes of breast cancer (based on ER and HER2).
Our specific aims are to use these adapted models to: 1) compare the impact of observed practice patterns to the benefits and harms of targeting new screening and adjuvant therapy modalities based on risk factors and molecular subtypes;2) explore the impact of improving access to new services;3) conduct value-of information-like analyses to evaluate the relationship between performance characteristics of a new screening test (e.g. blood based biomarker) and its impact on breast cancer mortality, utilization of treatments and over-diagnosis;and 4) communicate results to end-users using a web-based platform. This work will advance the field of modeling by explicitly capturing molecular attributes of breast cancer, and in so doing, build a robust capacity to inform debates about """"""""best practices"""""""" for cancer control interventions.

Public Health Relevance

Breast cancer is the 2 leading cause of cancer death in US women. New discoveries are re-defining best practices to reduce mortality. However, few of these novel strategies have been fully tested for effectiveness in reducing overall breast cancer deaths in the general population. Moreover, many women remain without access to even current standard cancer-related services. We will use population modeling to fill gaps in knowledge by translating research results from trials to their net effects on US breast cancer mortality rates.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1-SRLB-4 (M1))
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Stedman, Margaret R
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Georgetown University
Internal Medicine/Medicine
Schools of Medicine
United States
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Lieu, Tracy A; Ray, G Thomas; Prausnitz, Stephanie R et al. (2017) Oncologist and organizational factors associated with variation in breast cancer multigene testing. Breast Cancer Res Treat 163:167-176
Mandelblatt, Jeanne S; Ramsey, Scott D; Lieu, Tracy A et al. (2017) Evaluating Frameworks That Provide Value Measures for Health Care Interventions. Value Health 20:185-192
Huang, Xuelin; Li, Yisheng; Song, Juhee et al. (2017) A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality. Med Decis Making :272989X17714473
Flores, Kristina G; Steffen, Laurie E; McLouth, Christopher J et al. (2017) Factors Associated with Interest in Gene-Panel Testing and Risk Communication Preferences in Women from BRCA1/2 Negative Families. J Genet Couns 26:480-490
Chang, Yaojen; Near, Aimee M; Butler, Karin M et al. (2016) Economic Evaluation Alongside a Clinical Trial of Telephone Versus In-Person Genetic Counseling for BRCA1/2 Mutations in Geographically Underserved Areas. J Oncol Pract 12:59, e1-13
Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K et al. (2016) Using Active Learning for Speeding up Calibration in Simulation Models. Med Decis Making 36:581-93
Wen, Sijin; Huang, Xuelin; Frankowski, Ralph F et al. (2016) A Bayesian multivariate joint frailty model for disease recurrences and survival. Stat Med 35:4794-4812
Trentham-Dietz, Amy; Kerlikowske, Karla; Stout, Natasha K et al. (2016) Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes. Ann Intern Med 165:700-712
Miglioretti, Diana L; Lange, Jane; van den Broek, Jeroen J et al. (2016) Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study. Ann Intern Med 164:205-14
Huang, Xuelin; Yan, Fangrong; Ning, Jing et al. (2016) A two-stage approach for dynamic prediction of time-to-event distributions. Stat Med 35:2167-82

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