|Grant Number:||2R01CA094069-11A1 Interpret this number|
|Primary Investigator:||Whittemore, Alice|
|Project Title:||Statistical Methods for Genetic Epidemiology|
DESCRIPTION (provided by applicant): 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 is 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 is 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 is to augment the freely available R-based software RMAP (Risk Model Assessment Program) http://www.stanford.edu/~ggong/rmap/index.html to include the new and more informative performance measures and allow investigators to apply them to a broad range of epidemiologic data.
Performance of prediction models for BRCA mutation carriage in three racial/ethnic groups: findings from the Northern California Breast Cancer Family Registry.
Authors: Kurian AW, Gong GD, John EM, Miron A, Felberg A, Phipps AI, West DW, Whittemore AS
Source: Cancer Epidemiol Biomarkers Prev, 2009 Apr;18(4), p. 1084-91.
EPub date: 2009 Mar 31.
Prevalence of pathogenic BRCA1 mutation carriers in 5 US racial/ethnic groups.
Authors: John EM, Miron A, Gong G, Phipps AI, Felberg A, Li FP, West DW, Whittemore AS
Source: JAMA, 2007 Dec 26;298(24), p. 2869-76.
Significance levels for studies with correlated test statistics.
Authors: Shi J, Levinson DF, Whittemore AS
Source: Biostatistics, 2008 Jul;9(3), p. 458-66.
EPub date: 2007 Dec 18.
Comparison of admixture and association mapping in admixed families.
Authors: Clarke G, Whittemore AS
Source: Genet Epidemiol, 2007 Nov;31(7), p. 763-75.
Assessing environmental modifiers of disease risk associated with rare mutations.
Authors: Whittemore AS
Source: Hum Hered, 2007;63(2), p. 134-43.
EPub date: 2007 Feb 2.
Sex steroid hormones in young manhood and the risk of subsequent prostate cancer: a longitudinal study in African-Americans and Caucasians (United States).
Authors: Tsai CJ, Cohn BA, Cirillo PM, Feldman D, Stanczyk FZ, Whittemore AS
Source: Cancer Causes Control, 2006 Dec;17(10), p. 1237-44.
Nonparametric linkage analysis using person-specific covariates.
Authors: Whittemore AS, Halpern J
Source: Genet Epidemiol, 2006 Jul;30(5), p. 369-79.
Getting more from digital SNP data.
Authors: El Karoui N, Zhou W, Whittemore AS
Source: Stat Med, 2006 Sep 30;25(18), p. 3124-33.
Prostate specific antigen levels in young adulthood predict prostate cancer risk: results from a cohort of Black and White Americans.
Authors: Whittemore AS, Cirillo PM, Feldman D, Cohn BA
Source: J Urol, 2005 Sep;174(3), p. 872-6; discussion 876.
Prevalence of BRCA1 mutation carriers among U.S. non-Hispanic Whites.
Authors: Whittemore AS, Gong G, John EM, McGuire V, Li FP, Ostrow KL, Dicioccio R, Felberg A, West DW
Source: Cancer Epidemiol Biomarkers Prev, 2004 Dec;13(12), p. 2078-83.
Covariate adjustment in family-based association studies.
Authors: Whittemore AS, Halpern J, Ahsan H
Source: Genet Epidemiol, 2005 Apr;28(3), p. 244-55.
Classifying disease chromosomes arising from multiple founders, with application to fine-scale haplotype mapping.
Authors: Yu K, Martin RB, Whittemore AS
Source: Genet Epidemiol, 2004 Nov;27(3), p. 173-81.
Genetic association tests for family data with missing parental genotypes: a comparison.
Authors: Whittemore AS, Halpern J
Source: Genet Epidemiol, 2003 Jul;25(1), p. 80-91.
Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene.
Authors: Gong G, Whittemore AS
Source: Genet Epidemiol, 2003 Apr;24(3), p. 173-80.