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Using Secondary Data Analysis to Determine Whether Preventive Interventions Implemented Earlier in Life Reduce Suicide Risk (U01 Clinical Trial Not Allowed)

Sector: Hospital • Location: United States of America

Source: Grants.gov

Project
Closed

The purpose of this funding opportunity announcement (FOA) is to encourage research to integrate/harmonize existing large prevention trial data sets implemented earlier in life to examine whether they reduce risk for later suicide, including suicide thoughts and behaviorsfatal and nonfatal (STB), and related external injuries (e.g., nonfatal SUD/OUD/accidents, and all-cause mortality (e.g., Nation

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The project “Using Secondary Data Analysis to Determine Whether Preventive Interventions Implemented Earlier in Life Reduce Suicide Risk (U01 Clinical Trial Not Allowed)” is an infrastructure initiative in the Hospital sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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closed

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Description

Description

The purpose of this funding opportunity announcement (FOA) is to encourage research to integrate/harmonize existing large prevention trial data sets implemented earlier in life to examine whether they reduce risk for later suicide, including suicide thoughts and behaviorsfatal and nonfatal (STB), and related external injuries (e.g., nonfatal SUD/OUD/accidents, and all-cause mortality (e.g., National Death Index). This U01 will be competed and funded to: 1) obtain existing prevention trial data sets, with collaboration of the PIs who have the original data sets; 2) anonymize and aggregate and harmonize prevention trial data sets; 3) provide methodological and statistical expertise needed to examine, using theory-based approaches, whether intervening early reduces risk for STB and mortality, and potential mediators and moderators; and 4) oversee a final harmonized prevention trial data that will be deposited into the NIMH Data Archive (NDA). This harmonized dataset will serve as an ongoing resource to the research community for years to come.

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Source reliability

High

Data quality score

100%

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