? Biostatistics and Data Management Core The Biostatistics and Data Management (BDM) Core will support all projects and the Comparative Effectiveness (CE) Core by providing centralized coordination of high-quality data collection, management, analysis, and sharing. The BDM Core includes six Breast Cancer Surveillance Consortium (BCSC) breast imaging registries and a central Statistical Coordinating Center (SCC). The core will increase efficiency by providing projects with data collected from a common cohort of women and sharing novel statistical methods performed by a single analytical support team.
The specific aims are to: (1) collect, pool, manage, and share high-quality data required to meet project aims; (2) collaborate with program investigators in implementing sound and efficient study designs and perform valid statistical analyses that address each project aim; and (3) develop and implement novel statistical methods needed to meet study aims. The BDM Core will accomplish these aims using existing and new data collected on 2.6 million women (including 90,000 survivors with surveillance imaging) by six breast imaging registries. These BCSC registries have built strong relationships with the participating breast imaging facilities and have established secure procedures for data collection and linkage. Data will be pooled and analyzed by the SCC, which has extensive experience in data management, analyzing complex longitudinal observational data, and developing novel statistical methods as needed to overcome the limitations of existing methods. Following the BCSC's well-established collaborative research policies and procedures, the BCM Core will continue its successes in sharing data with the broader research community. Using the unique set of resources available to the BCSC ? the established and cohesive research network; integrated surveillance system of longitudinal, individual-level data on women, radiologists, and facilities; infrastructure for accessing well-characterized study populations; and expertise in statistical methods and outcomes measurement in community settings ? the BCSC registries and SCC will continue working together to support this renewal in answering key questions that cannot be addressed otherwise. These combined strengths ideally position the program's research teams to determine the most effective breast cancer screening and surveillance strategies in community practice.
? Biostatistics and Data Management Core The Biostatistics and Data Management Core will collect, manage, analyze, and share data for all projects. Data collected on 2.6 million women (including 90,000 breast cancer survivors) from six breast imaging registries will be combined into common datasets that will be analyzed at a central Statistical Coordinating Center and made accessible to all projects and cores. Core staff will work closely with program investigators to ensure high-quality and efficient research practices.
| Lee, Janie M; Abraham, Linn; Lam, Diana L et al. (2018) Cumulative Risk Distribution for Interval Invasive Second Breast Cancers After Negative Surveillance Mammography. J Clin Oncol 36:2070-2077 |
| 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 |
| Puvanesarajah, Samantha; Nyante, Sarah J; Kuzmiak, Cherie M et al. (2018) PAM50 and Risk of Recurrence Scores for Interval Breast Cancers. Cancer Prev Res (Phila) 11:327-336 |
| 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 |
| Dabbous, Firas; Dolecek, Therese A; Friedewald, Sarah M et al. (2018) Performance characteristics of digital vs film screen mammography in community practice. Breast J 24:369-372 |
| 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 |
| Rice, Megan S; Tamimi, Rulla M; Bertrand, Kimberly A et al. (2018) Does mammographic density mediate risk factor associations with breast cancer? An analysis by tumor characteristics. Breast Cancer Res Treat 170:129-141 |
| Ray, Kimberly M; Kerlikowske, Karla; Lobach, Iryna V et al. (2018) Effect of Background Parenchymal Enhancement on Breast MR Imaging Interpretive Performance in Community-based Practices. Radiology 286:822-829 |
| He, Xiaofei; Schifferdecker, Karen E; Ozanne, Elissa M et al. (2018) How Do Women View Risk-Based Mammography Screening? A Qualitative Study. J Gen Intern Med 33:1905-1912 |
| Onega, T; Zhu, W; Weiss, J E et al. (2018) Preoperative breast MRI and mortality in older women with breast cancer. Breast Cancer Res Treat 170:149-157 |
Showing the most recent 10 out of 190 publications
