The Research Core will serve as the nucleus connecting the partnering institutions'research activities while incubating new research ideas. The theme of the Core, as well as of the theme of the entire National Transdisciplinary Collaborative Center for African American Men's Health (NTCC), is the reduction of disparities related to African American men's health over the life course. Our objective is to advance the understanding of racial/ethnic disparities as they relate to African American men's health throughout the life course, with the goal of developing and implementing interventions to address these disparities. We will pursue this objective in two ways: 1) by developing and testing interventions directed at the most important causes of morbidity and mortality at various stages during the life course of African American males;and 2) by studying the development and progression of risk factors for the main causes of morbidity and mortality of African American males over the life course, as well as factors related to racial/ethnic health disparities, utilizing data from ongoing longitudinal epidemiological studies. Our ultimate goal is to reduce and eventually eliminate racial/ethnic disparities related to African American men's health. We will build on the success of the research conducted by our NIMHD-funded Centers of Excellence (COEs), our Enhancing Minority Participation in Clinical Trials (EMPaCT) U24 National Consortium, and our history of partnership in the past several years. Our COEs and the EMPaCT Consortium will also provide an infrastructure and facilities to leverage the Cores/Programs proposed in this application, to ensure the success of the proposed and future research.

National Institute of Health (NIH)
National Institute on Minority Health and Health Disparities (NIMHD)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZMD1-RN)
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University of Alabama Birmingham
United States
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Chen, Liddy M; Ibrahim, Joseph G; Chu, Haitao (2014) Sample size determination in shared frailty models for multivariate time-to-event data. J Biopharm Stat 24:908-23
Chen, Yong; Liu, Yulun; Ning, Jing et al. (2014) A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews. Stat Methods Med Res :
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