NIH grant: Leveraging Real World Data to Advance Behavioral and Social Sciences Research (1R25HL184663-01)

retained text

NIH grant 1R25HL184663-01: Leveraging Real World Data to Advance Behavioral and Social Sciences Research
Institute: {'code': 'HL', 'abbreviation': 'NHLBI', 'name': 'National Heart Lung and Blood Institute', 'admin_org_id': '12500068', 'admin_funding_url': ''}. PI: David H Rehkopf. Award: $206470.
Start: 2026-08-15T00:00:00.
ABSTRACT
Real-world data, data not collected primarily for research (e.g., medical billing data, electronic health record
data), can open new frontiers in behavioral and social sciences research given that this data typically 1) has a
large sample size, allowing greater statistical power and the ability to look at subgroup effects, 2) includes a
broader population, not just those who respond to surveys or who meet typically strict trial inclusion criteria,
and 3) is updated in real-time for more rapidly actionable insights. While training in data analysis is a standard
part of quantitative social science curricula, there are few training opportunities focused specifically on
developing skills to use real-world data effectively to answer behavioral and social science research questions.
To meet this critical unmet need, we will create and evaluate the impact of a new innovative course, supported
by substantive data resources, designed to build the capacity of early career scholars to use real-world data to
advance behavioral and social sciences research in the health sciences. Our specific aims are: Aim 1: Develop
a comprehensive portfolio of real-world data training modules that include a range of topics crucial to
advancing chronic disease research in the behavioral and social sciences. The course will have a flexible
design, adding and modifying modules based on the needs and interests of each course cohort. Aim 2: Create
and implement a 9-month course that builds the real-world data skills of early career scholars, starting with a 1-
week in-person immersive session and continuing with monthly remote sessions. Aim 3: Train and mentor five
emerging real-world data leaders (Real World Data Leads) annually, representing both emerging and
established behavioral and social science health research centers and programs, to develop and teach real-
world data training sessions at their home institutions. Aim 4: Develop and provide access to sustainable data
resources that support the course and accelerate the use of real-world data for behavioral and social sciences
research in the health sciences. The achievement of these aims will increase the availability of real-world
training programs across the United States, build data capacity across institutions, create a cadre of leaders
who can continue to drive efforts forward, provide essential data resources that reduce barriers to use, and
increase the real-world data skills of early career scholars. This will quickly accelerate the use of real-world
data, sparking the exploration of novel research questions that advance behavioral and social sciences
research in health.

document fetched 2026-08-23 06:34