We continued to develop, refine and evaluate the National Cancer Institutes Breast Cancer Risk Assessment Tool (BCRAT). Dr. Mateo Banegas, a NCI Cancer Prevention Fellow, developed a new model for absolute invasive breast cancer risk for Latina women. There is interest in determining whether adding information from single nucleotide polymorphisms (SNPs) can increase the discriminatory accuracy and usefulness for screening of risk models. We demonstrated improved risk stratification of breast cancer by adding SNPs to a variety of more standard risk factors. Using data from 1.4 million women undergoing HPV testing and Pap smears in Kaiser Permanente Northern California (KPNC), we published on: (1) estimating absolute risks based on HPV genotyping tests; (2) calculating and comparing risk versus those in the New Mexico HPV/Pap Registry, and (3) calculating risks among those with equivocal Pap smears. We developed and validated models for risk of lung cancer incidence and death and used them to project the impact of risk-based selection of smokers for CT lung-cancer screening in the US. We developed absolute risk models appropriate under left/interval/right-censoring data occurring for screen-detected disease, which occurs for electronic health record data, called the logistic-Weibull and the logistic-Cox models. We explained why a method sometimes used to assess absolute risk models in case-control data does not assess calibration, but only fits of relative risk.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIACP010188-15
Application #
10007432
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
15
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Division of Cancer Epidemiology and Genetics
Department
Type
DUNS #
City
State
Country
Zip Code
Cheung, Li C; Pan, Qing; Hyun, Noorie et al. (2017) Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records. Stat Med 36:3583-3595
Katki, Hormuzd A; Kovalchik, Stephanie A; Berg, Christine D et al. (2016) Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening. JAMA 315:2300-11
Kovalchik, Stephanie A; Pfeiffer, Ruth M (2014) Population-based absolute risk estimation with survey data. Lifetime Data Anal 20:252-75
Pfeiffer, Ruth M; Park, Yikyung; Kreimer, Aimée R et al. (2013) Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 10:e1001492
Pfeiffer, Ruth M (2013) Extensions of criteria for evaluating risk prediction models for public health applications. Biostatistics 14:366-81
Riedl, Regina; Engels, Eric A; Warren, Joan L et al. (2013) Blood transfusions and the subsequent risk of cancers in the United States elderly. Transfusion 53:2198-206
Kovalchik, Stephanie A; Ronckers, Cécile M; Veiga, Lene H S et al. (2013) Absolute risk prediction of second primary thyroid cancer among 5-year survivors of childhood cancer. J Clin Oncol 31:119-27
Kovalchik, Stephanie A; Pfeiffer, Ruth M (2012) Re: Assessment of impact of outmigration on incidence of second primary neoplasms in childhood cancer survivors estimated from SEER data. J Natl Cancer Inst 104:1517-8
Gail, Mitchell H (2011) Personalized estimates of breast cancer risk in clinical practice and public health. Stat Med 30:1090-104
Pfeiffer, R M; Gail, M H (2011) Two criteria for evaluating risk prediction models. Biometrics 67:1057-65

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