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)
Project #
Application #
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Dunn, Michelle C
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stanford University
Schools of Medicine
United States
Zip Code
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
Zhou, Baiyu; Shi, Jianxin; Whittemore, Alice S (2011) Optimal methods for meta-analysis of genome-wide association studies. Genet Epidemiol 35:581-91
Whittemore, Alice S (2010) Evaluating health risk models. Stat Med 29:2438-52
Gong, Gail; Hannon, Nathan; Whittemore, Alice S (2010) Estimating gene penetrance from family data. Genet Epidemiol 34:373-81
Kurian, Allison W; Gong, Gail D; John, Esther M et al. (2009) Performance of prediction models for BRCA mutation carriage in three racial/ethnic groups: findings from the Northern California Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev 18:1084-91
Shi, Jianxin; Levinson, Douglas F; Whittemore, Alice S (2008) Significance levels for studies with correlated test statistics. Biostatistics 9:458-66
John, Esther M; Miron, Alexander; Gong, Gail et al. (2007) Prevalence of pathogenic BRCA1 mutation carriers in 5 US racial/ethnic groups. JAMA 298:2869-76
Clarke, Geraldine; Whittemore, Alice S (2007) Comparison of admixture and association mapping in admixed families. Genet Epidemiol 31:763-75
Whittemore, Alice S (2007) Assessing environmental modifiers of disease risk associated with rare mutations. Hum Hered 63:134-43

Showing the most recent 10 out of 22 publications