This application requests renewal of funding to serve as the Statistical and Data Management Center of the AIDS Clinical Trials Group (ACTG). The ACTG is an international clinical trials network which is proposing to address objectives in NIAID high priority research areas for adults living with HIV. Specifically: to evaluate novel and durable interventions targeting HIV infection; to identify strategies to reduce HIV reservoirs and control HIV replication in the absence of ART (?ART-free remission?); to improve the diagnosis and treatment of tuberculosis, especially in those co-infected with HIV; and to improve the prevention and treatment of co-morbidities and co-infections, including curative strategies hepatitis B virus co-infection. The application's specific aims and long-term objectives are: to support the international research agenda of the ACTG by providing a group of highly-experienced statisticians, epidemiologists, data managers and other professionals who have a deep interest in research in HIV infection and its complications and co-infections; to support the development, conduct and analysis of ACTG studies with expert statistical and data management leadership and high quality clinical and laboratory data management and communications systems which comply with regulatory requirements, meet industry standards and are fully integrated into quality management system workflows; and to advance the mission of the ACTG through innovation in study design and analysis and the development of efficient methods and systems, that facilitate the work of the ACTG and promote harmonization and data sharing with collaborating organizations. The proposed SDMC consists of two components. One is a Statistical and Data Analysis Center at Harvard T.H. Chan School of Public Health which will provide biostatistical leadership, and includes a subcontract to University of California San Francisco to support additional tuberculosis trial design expertise. The second is a Data Management Center at Frontier Science & Technology Research Foundation which will provide expertise and leadership in data management systems, and has primary responsibility for data collection, quality and integrity.

Public Health Relevance

The expertise provided by the SDMC will help advance research concerning the treatment of adults living with HIV and co-infections such as tuberculosis and hepatitis B virus. It will achieve this by ensuring that ACTG clinical trials and other studies are designed, conducted, and analyzed efficiently and to the highest standards using state-of-the-art, compliant clinical and laboratory information management systems that ensure complete and high-quality data. This research will continue to make significant contributions in advancing optimal treatment of people living with HIV both in the United States and internationally.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project with Complex Structure Cooperative Agreement (UM1)
Project #
2UM1AI068634-15
Application #
9987138
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Ojumu, Akinlolu O
Project Start
2006-06-29
Project End
2027-11-30
Budget Start
2020-12-01
Budget End
2021-11-30
Support Year
15
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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Taiwo, Babafemi O; Zheng, Lu; Stefanescu, Andrei et al. (2018) ACTG A5353: A Pilot Study of Dolutegravir Plus Lamivudine for Initial Treatment of Human Immunodeficiency Virus-1 (HIV-1)-infected Participants With HIV-1 RNA <500000 Copies/mL. Clin Infect Dis 66:1689-1697
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Kearney, Mary F; Spindler, Jonathan; Wiegand, Ann et al. (2018) Lower pre-ART intra-participant HIV-1 pol diversity may not be associated with virologic failure in adults. PLoS One 13:e0190438
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Hart, Stephen A; Vardhanabhuti, Saran; Strobino, Sarah A et al. (2018) Impact of Changes Over Time in the Stanford University Genotypic Resistance Interpretation Algorithm. J Acquir Immune Defic Syndr 79:e21-e29

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