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.
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