Opportunity

Simpler Grants.gov #PAR-25-095

NIH Grant for Secondary Analysis and Integration of Existing Data on Cancer Risk

Buyer

National Institutes of Health

Posted

November 18, 2024

Respond By

September 07, 2026

Identifier

PAR-25-095

NAICS

541715

This opportunity from the National Institutes of Health (NIH), led by the National Cancer Institute (NCI), invites applications for research grants focused on secondary analysis and integration of existing data to advance understanding of cancer risk and related outcomes. - Government Buyer: - National Institutes of Health (NIH) - National Cancer Institute (NCI) and other participating Institutes - Products/Services Requested: - Secondary analysis and integration of existing datasets and database resources - Research to elucidate cancer risk, risk prediction, reduction, survival, and treatment response - Use of clinical, environmental, surveillance, health services, vital statistics, behavioral, lifestyle, genomic, and molecular profile data - Innovative analytical methods and novel dataset combinations - Unique/Notable Requirements: - Applicants must leverage existing data (no new data collection) - Supports new research aims, advanced analytics, and integration of diverse datasets - Broad eligibility: nonprofits, government, educational, business, tribal, and foreign organizations - Maximum award amount is $350,000 - No specific OEMs or vendors are named, as this is a research grant opportunity

Description

This funding opportunity from the National Cancer Institute encourages applications proposing 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, behavioral, genomic, and molecular data to address key scientific questions in cancer research. Applicants may propose new research aims using existing data, advanced analytical methods, or novel dataset integrations to explore important cancer-related scientific questions.

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