Data Management and Analysis Core (DMAC)

BACKGROUND

The RISE Data Management and Analysis Core (DMAC) will advance the goals of the proposed Program by designing, developing, implementing, and overseeing data infrastructure and analysis for all Projects in communication with the Scientific Administrative Core (SAC) and Dissemination and Engagement Core (DEC). The DMAC will conduct integral services to support each project, including but not limited to:

• Project 1 (IMAGE): Designing data collection systems and procedures for neuroimaging among cohorts of CHEU and children with ARV exposure,

• Project 2 (BONDS): Providing multi-country clinical trial data infrastructure and DSMB reporting to evaluate the BONDS intervention package, and

• Project 3 (IMPLEMENT): Facilitating complex data merges for risk score validation predicting child neurodevelopmental delay

Across Projects 1 and 2, the DMAC will guide implementation, quality assurance, and analysis of neurodevelopmental assessments leveraging expertise from the Project teams. The DMAC will build data management and analysis capacity across all Research Project teams and sites in Botswana, Kenya, and Zimbabwe through annual regional training workshops in coordination with the Scientific Administrative Core (SAC). DMAC services will ensure timely and accurate dissemination of Program findings in collaboration with the DEC.

To conduct DMAC objectives, we have assembled an expert team with decades of collective experience in complex data management and rigorous statistical analysis from HIV clinical trials and longitudinal studies among mother-child cohorts in sub-Saharan Africa.

OBJECTIVES

Approach: The DMAC Data Systems & Management Section will provide customized data architecture, REDCap databases with shinyapp.io data dashboard platforms, data management standard operating procedure guidebooks, and data quality assurance and reporting services to Projects 1 and 2. The DMAC Neurodevelopmental & Mental Health Measurement Section will guide training, supervision, and data quality assurance on all neurodevelopmental and mental health assessment tools. The DMAC Neuroimaging Section will lead all MRI neuroimaging procedures, quality assurance, and storage.

Approach: The DMAC Data Capacity Building Section will evaluate expertise and growth areas across all Project teams and study sites, generating a plan for peer-to-peer and expert-led data management and analysis training. Capacity-building opportunities will include: 1) Project-specific neurodevelopmental and mental health measurement and neuroimaging training, 2) Best practices in data systems and management courses, 3) Statistical analysis methods lectures, 4) Manuscript development workshops and peer-writing groups.

Approach: The DMAC Statistical Analysis Section is comprised of experienced biostatisticians and epidemiologists with expertise in complex longitudinal, dyadic mother-child analyses. We will oversee the development of statistical analysis plans and conduct statistical analyses using relevant statistical software (e.g., R, Python, Stata) to address the specific aims of all Projects and arising exploratory, ancillary, or sensitivity analyses.

Approach: The DMAC will collaborate with Program MPIs, Project and Core Directors, and study site PIs to ensure consistent and appropriate data-related service delivery. In collaboration with the Administrative Core, we will coordinate all data-related training events and communicate data-related procedures and information. With the Dissemination & Engagement Core, we will ensure compliance with NIH policies on external sharing of data sources, promote data equity across Projects, and disseminate data and data-related products. Overall, the DMAC will facilitate data infrastructure and procedures necessary to optimize the health of children and adolescents with perinatal HIV exposure through the proposed Program.

TEAM

MULTIPLE PRINCIPAL INVESTIGATORS