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National Institutes of Health: National Cancer Institute: Division of Cancer Control and Population Sciences
Grant Details

Grant Number: 5R03CA150136-02 Interpret this number
Primary Investigator: Whittemore, Alice
Organization: Stanford University
Project Title: Validating Cancer Risk Models: a Pilot Study to Evaluate Cost-Efficient Methods
Fiscal Year: 2011
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Abstract

DESCRIPTION (provided by applicant): Validating Cancer Risk Models: a Pilot Study to Evaluate Cost-efficient Methods Abstract To facilitate targeted cancer prevention strategies and help patients weigh the costs and benefits of genetically tailored interventions, we need statistical models that provide accurate and precise projections of a person's risk of developing a given adverse outcome. Such models must be evaluated using cohort data on individuals at risk of the outcome. The ideal evaluation would genotype all subjects in a large cohort, use the genotypes to assign risks to all cohort members, and then relate assigned risks to subsequent outcomes. However such complete genotyping is too costly to be feasible. Our goal here is to test two new cost-effective methods for evaluating the added value of genetic covariates. The first is a case-cohort design that genotypes only a subset of the cohort members. The second uses case-control data and Bayes Rule to estimate the amount of increased precision gained with genotyping. Our goal is to use cohort data from the California Teachers Study (CTS) and population-based case-control data to test the two methods. We will do so by evaluating existing risk models for cancers of the ovary and breast using data from: a) all CTS subjects; b) a subset of CTS subjects in a case- cohort design; and c) subjects from a case-control study. We will use BRCAPRO scores as surrogates for pathogenic mutations of BRCA1 and BRCA2. This will allow us to compare inferences using (b) and (c) with those from (a), which will form the gold standard. PUBLIC HEALTH RELEVANCE: Project Narrative To facilitate targeted cancer prevention strategies and help patients weigh the costs and benefits of genetically tailored interventions, we need statistical models that provide accurate and precise projections of a person's risk of developing a given adverse outcome. The goal of this project is to test two new cost-effective methods for evaluating the value of adding genetic covariates to these models using data from studies of cancers of the breast and ovary.

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Publications

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