Cost-effective cancer prevention and control strategies that emphasize population subgroups at highest risk are increasingly needed to counter national and global health costs. The proposed research will address this need by helping identify high-risk individuals in three specific aims.
The first aim i s to develop more informative measures of model performance that can focus on specific population subgroups. In particular, we will develop and evaluate better measures of model accuracy (calibration), and model discrimination between those likely and unlikely to develop the adverse outcome.
The second aim i s to develop new ways to expand the utility of epidemiologic data for assessing model performance. These methods will allow investigators to accommodate cohort selection bias in assessing model performance;use and interpret case-control data for assessing model discrimination;and assess risks of multiple competing adverse outcomes in the same individual.
The third aim i s to augment the freely available R-based software RMAP (Risk Model Assessment Program) to include the new and more informative performance measures and allow investigators to apply them to a broad range of epidemiologic data.

Public Health Relevance

Personal risk models offer the hope of identifying individuals at highest risk of adverse health outcomes, who could be targeted for cost-effective prevention strategies. Our goals are to develop new ways to evaluate the performance of such models and to illustrate the methods by applying them to site-specific cancer data. We plan to make the methods available to the research community through freely available software.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Epidemiology of Cancer Study Section (EPIC)
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Dunn, Michelle C
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Stanford University
Schools of Medicine
United States
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McGuire, Valerie; Hartge, Patricia; Liao, Linda M et al. (2016) Parity and Oral Contraceptive Use in Relation to Ovarian Cancer Risk in Older Women. Cancer Epidemiol Biomarkers Prev 25:1059-63
Ioannidis, Nilah M; Rothstein, Joseph H; Pejaver, Vikas et al. (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99:877-885
(2016) PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet 53:800-811
Quante, Anne S; Whittemore, Alice S; Shriver, Tom et al. (2015) Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk. J Natl Cancer Inst 107:
Sieh, Weiva; Rothstein, Joseph H; McGuire, Valerie et al. (2014) The role of genome sequencing in personalized breast cancer prevention. Cancer Epidemiol Biomarkers Prev 23:2322-7
Ahsan, Habibul; Halpern, Jerry; Kibriya, Muhammad G et al. (2014) A genome-wide association study of early-onset breast cancer identifies PFKM as a novel breast cancer gene and supports a common genetic spectrum for breast cancer at any age. Cancer Epidemiol Biomarkers Prev 23:658-69
Gong, Gail; Quante, Anne S; Terry, Mary Beth et al. (2014) Assessing the goodness of fit of personal risk models. Stat Med 33:3179-90
Zhou, Baiyu; Shi, Jianxin; Whittemore, Alice S (2011) Optimal methods for meta-analysis of genome-wide association studies. Genet Epidemiol 35:581-91
Dite, G S; Whittemore, A S; Knight, J A et al. (2010) Increased cancer risks for relatives of very early-onset breast cancer cases with and without BRCA1 and BRCA2 mutations. Br J Cancer 103:1103-8
Whittemore, Alice S (2010) Evaluating health risk models. Stat Med 29:2438-52

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