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Modeling Breast Carcinoma In Situ: Implications for Prevention and Control


Biography

Overview
The incidence of early stage breast cancer including in situ disease has increased rapidly over the past decade primarily due to increasing use of screening mammography. Concern exists regarding the potential for over-diagnosis of early stage breast cancer. Our understanding of the natural history and progression of in situ disease to invasive breast cancer is incomplete. Few population-based models exist to explore hypotheses about the natural history of in situ disease. This research proposes a series of analyses designed to further our understanding in this regard and to inform decision making about prevention and screening strategies to control breast cancer. Our first aim is to extend an existing U.S. population-based discrete-event simulation model of breast cancer to account for more detailed information about in situ disease. We will evaluate the consistency of hypotheses about the natural history and progression of breast carcinoma in situ with observed data. This model will then be used to examine health trade-offs at both an individual and population level associated with different prevention and control strategies. For this grant specifically, we propose to estimate the potential for over-diagnosis and subsequent over-treatment of early stage breast cancer that is associated with increased screening. The result of this work will be a quantitative modeling platform useful in informing decision-making about breast cancer prevention and control at two levels: an individual facing screening and treatment decisions, and society setting policy and guiding resource allocation. The proposed research brings the strengths of a decision-analytic approach to clinical and policy questions in breast cancer. Our methodologies and analyses have relevance for other types of cancer.
F32CA125984
STOUT, NATASHA KAY

Time
2006-11-16
2009-11-15
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.