title: Frailty Models for Familial Risk with Application to Breast Cancer.
abstract: In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.
authors.affiliation: Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Technion City, Haifa 32000, Israel.
authors.forename: Malka
authors.initials: M
authors.lastname: Gorfine
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, U.S.A.
authors.forename: Li
authors.initials: L
authors.lastname: Hsu
authors.affiliation: Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, U.S.A.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
keywords: ROC analysis; breast cancer; familial risk prediction; frailty model; multivariate survival; risk index