Opportunity
Simpler Grants.gov #PAR-25-096
NIH Grant for Secondary Analysis and Integration of Existing Data to Elucidate Cancer Risk and Related Outcomes
Buyer
National Institutes of Health
Posted
November 18, 2024
Respond By
September 07, 2026
Identifier
PAR-25-096
NAICS
541715
The National Institutes of Health (NIH), through the National Cancer Institute (NCI), is seeking applications for a grant focused on secondary analysis and integration of existing data to advance cancer risk research. - Government Buyer: - National Institutes of Health (NIH), National Cancer Institute (NCI) - Other participating NIH institutes under assistance codes 93.866, 93.172, 93.393, and 93.121 - Products/Services Requested: - Research services for secondary analysis and integration of existing datasets - Includes clinical, environmental, surveillance, health services, vital statistics, behavioral, lifestyle, genomic, and molecular data - Applicants may propose new research aims, advanced analytical methods, or novel dataset integrations - Unique/Notable Requirements: - No specific products or part numbers are requested; this is a research funding opportunity - Open to a wide range of organizations: nonprofits, businesses, government, educational, and foreign entities - Minimum program funding is $200,000 - No cost sharing or matching required - OEMs/Vendors: - No OEMs or commercial vendors specified, as this is a grant for research services - Place of Performance: - National Institutes of Health (NIH)
Description
This funding opportunity from the National Cancer Institute (NCI) encourages applications for secondary data analysis and integration of existing datasets to elucidate cancer risk and related outcomes such as risk prediction, survival, or treatment response. The initiative supports analysis of existing clinical, environmental, surveillance, health services, vital statistics, behavioral, lifestyle, genomic, and molecular data. Applicants may propose new research aims using existing data, advanced analytical methods, or novel dataset integrations to explore important scientific questions in cancer research.