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, is currently developing a new model for absolute invasive breast cancer risk for Latina women. Breast, endometrial and ovarian cancers share a hormonal etiology and epidemiologic risk factors. We used data on white, non-Hispanic women over age 50 years from the Prostate, Lung, Colorectal, and Ovary (PLCO) Cancer Screening Trial and the AARP-NIH Diet and Health Study and data from NCI's SEER Program, we developed and published models to estimate a womans absolute risk of developing breast, endometrial or ovarian cancer over specific intervals. Some women have risks of endometrial cancer comparable to or higher than their breast cancer risks. 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 published data showing that huge samples sizes are needed in genome-wide association studies (GWAS) to achieve the full potential discriminatory accuracy inherent in SNPs. We published research showing that with smaller GWAS samples, one should rarely include more than 100 SNPs in building risk models. Using data from 1.4 million women undergoing HPV testing and Pap smears in Kaiser Permanente Northern California (KPNC), we published a paper estimating absolute risks to women who undergo HPV testing alone (without Pap smears). These risks were evidence considered by the FDA staff when they decided to allow HPV testing without Pap smears. We demonstrated that absolute risks following HPV-negative/ASC-US were very low. We calculated age-specific risks for women with newly-acquired HPV infections. We constructed a bivariate model of the risk of cervical cancer and the chance of clearance of an HPV infection that could be useful in developing future cervical screening guidelines. We previously published two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF(q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF(p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. We published extensions of these criteria obtained by integrating PCF(q) and PNF(p) over ranges of q and p. We also developed methods of estimating PCF(q) and PNF(p) and their integrated forms both when the risk model was assumed to be well calibrated, and on the basis of empirical data on health outcomes. The latter methods are valid even when the risk models are not well calibrated, but they yield less precise estimates. We developed and published approaches for estimating and performing inference on absolute risk based on representative survey data, such as the National Health and Nutrition Examination Survey (NHANES) (in press). Using influence functions, we derived variance estimates that are valid for surveys with weighting and cluster sampling. We also proposed a criterion to estimate the importance of each competing cause on the calculation of the absolute risk of a particular cause.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIACP010188-10
Application #
8938256
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
10
Fiscal Year
2014
Total Cost
Indirect Cost
Name
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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