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Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis (R01)

Sector: Warehouse • Location: United States of America

Source: Grants.gov

Project
Archived

-The National Cancer Institute (NCI) has identified risk prediction as an area of extraordinary opportunity in NCI s 2006 Plan and Budget Proposal: The Nation's Investment in Cancer Research ( explore this opportunity, the NCI Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Treatment and Diagnosis (DCTD) solicit applications for

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The project “Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis (R01)” is an infrastructure initiative in the Warehouse sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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Description

Description

-The National Cancer Institute (NCI) has identified risk prediction as an area of extraordinary opportunity in NCI s 2006 Plan and Budget Proposal: The Nation's Investment in Cancer Research (http://plan2006.cancer.gov/).To explore this opportunity, the NCI Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Treatment and Diagnosis (DCTD) solicit applications for research projects to develop, apply, and evaluate new and existing cancer risk and prognostic prediction models for use by researchers, clinicians, and the general public. -The purpose of this Funding Opportunity Announcement (FOA) is to encourage clinicians, epidemiologists, geneticists, statisticians, and translational researchers working in the field of cancer control and prevention to improve existing models for cancer risk and prognosis by developing innovative research projects that use existing data; develop new models for cancer risk and prognosis; and validate new models and evaluate their utility in research and clinic settings. Applications that are focused on the identification and characterization of prognostic/diagnostic markers are NOT responsive to this FOA. -This FOA is designed to provide a mechanism of support for investigators to address two major challenges in model development, which are: integrating diverse types of data (e.g., clinical, demographic, pathologic, environmental, epidemiologic, outcomes, and genetic data from varied data marts or warehouses); and ensuring adequate validation (i.e., using multiple separate populations to define sensitivity, specificity, and positive and negative predictive values).

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Source reliability

High

Data quality score

100%

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URL

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