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.
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.
|Yan, Fangrong; Zhu, Huihong; Liu, Junlin et al. (2018) Design and inference for 3-stage bioequivalence testing with serial sampling data. Pharm Stat 17:458-476|
|van den Broek, Jeroen J; van Ravesteyn, Nicolien T; Mandelblatt, Jeanne S et al. (2018) Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology. Med Decis Making 38:112S-125S|
|Alagoz, Oguzhan; Berry, Donald A; de Koning, Harry J et al. (2018) Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models. Med Decis Making 38:3S-8S|
|Mandelblatt, Jeanne S; Near, Aimee M; Miglioretti, Diana L et al. (2018) Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling. Med Decis Making 38:9S-23S|
|Gangnon, Ronald E; Stout, Natasha K; Alagoz, Oguzhan et al. (2018) Contribution of Breast Cancer to Overall Mortality for US Women. Med Decis Making 38:24S-31S|
|Alagoz, Oguzhan; Ergun, Mehmet Ali; Cevik, Mucahit et al. (2018) The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update. Med Decis Making 38:99S-111S|
|van den Broek, Jeroen J; van Ravesteyn, Nicolien T; Heijnsdijk, Eveline A et al. (2018) Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia. Med Decis Making 38:54S-65S|
|Munoz, Diego F; Xu, Cong; Plevritis, Sylvia K (2018) A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010. Med Decis Making 38:89S-98S|
|Choi, Sangbum; Kang, Sangwook; Huang, Xuelin (2018) Smoothed quantile regression analysis of competing risks. Biom J 60:934-946|
|Schechter, Clyde B; Near, Aimee M; Jayasekera, Jinani et al. (2018) Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model. Med Decis Making 38:66S-77S|
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