Secondary Analysis and Integration of Existing Data to Elucidate Cancer Risk and Related Outcomes (R01 Clinical Trial Not Allowed)

The Secondary Analysis and Integration of Existing Data to Elucidate Cancer Risk and Related Outcomes funding opportunity is administered by the National Institutes of Health through multiple participating institutes including the National Cancer Institute, National Human Genome Research Institute, National Institute on Aging, and National Institute of Dental and Craniofacial Research. This initiative is structured as an R01 Research Project Grant and reflects NIH’s broader commitment to maximizing the use of existing biomedical and population health data. The program aligns with federal priorities to advance cancer research by leveraging previously collected datasets and improving data accessibility, interoperability, and reuse across the scientific community. The primary purpose of this funding opportunity is to support innovative secondary data analysis that advances understanding of cancer risk, progression, prevention, and outcomes. Applicants are expected to utilize existing datasets such as clinical records, genomic databases, epidemiological studies, and behavioral or environmental data sources. The initiative emphasizes generating new insights without collecting substantial new data, instead encouraging novel analytical approaches, integration of multiple datasets, and development of advanced computational or statistical methods. Projects may explore cancer etiology, screening, survivorship, treatment response, and disparities in outcomes across populations. Funding supports research activities that are cost-effective and data-driven, with a maximum of ten percent of the project budget allowed for limited new data generation strictly for validation purposes. The award structure allows up to three hundred fifty thousand dollars in direct costs per year for a project period of up to five years. Applicants must ensure that their proposed work is distinct from the original aims of the datasets being used and that all data usage complies with human subjects protections and data sharing policies. Awardees are expected to share cleaned datasets, analytical tools, and documentation broadly with the research community in accordance with NIH data management and sharing requirements. Eligible applicants include a wide range of domestic and international entities such as higher education institutions, nonprofits, for-profit organizations, and government agencies at various levels. The program permits multiple applications from the same organization provided each is scientifically distinct. However, applications involving foreign subawards are not permitted under this announcement. Principal investigators must have the appropriate expertise and institutional support to conduct the proposed research and must comply with all required federal registrations prior to submission. Applications must be submitted electronically through NIH-approved systems such as Grants.gov Workspace or the NIH ASSIST platform. The submission process requires adherence to the NIH Application Guide, including detailed research strategies, data management plans, and compliance with formatting and content requirements. Applications undergo a rigorous peer review process evaluating significance, innovation, methodological rigor, feasibility, investigator expertise, and available resources. Additional review considerations include protections for human subjects, inclusion plans, and data validity. The funding opportunity follows standard NIH submission cycles with multiple due dates annually, typically in February, June, and October, and corresponding review and award timelines extending into subsequent months. Applications are due by five o’clock PM local time of the applicant organization on the specified dates. The program is recurring and remains active until its expiration date in November 2026. Prospective applicants are encouraged to engage with program officers to confirm alignment with institute priorities and to ensure that proposed research topics are responsive to the goals of the initiative.

Focus on integrating multiple datasets and demonstrating innovative analytical methods with strong feasibility and rigor; ensure compliance with NIH data sharing and methodological expectations