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Choi YH , Jung H , Buys S , Daly M , John EM , Hopper J , Andrulis I , Terry MB , Briollais L
A competing risks model with binary time varying covariates for estimation of breast cancer risks in BRCA1 families
Stat Methods Med Res. 2021 Jul 7 :9622802211008945
AbstractMammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy can potentially reduce breast cancer risks among mutation carriers of BRCA families. The evaluation of these interventions is usually complicated by the fact that their effects on breast cancer may change over time and by the presence of competing risks. We introduce a correlated competing risks model to model breast and ovarian cancer risks within BRCA1 families that accounts for time-varying covariates. Different parametric forms for the effects of time-varying covariates are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events.We also introduce a new ascertainment correction approach that accounts for the selection of families through probands affected with either breast or ovarian cancer, or unaffected. Our simulation studies demonstrate the good performances of our proposed approach in terms of bias and precision of the estimators of model parameters and cause-specific penetrances over different levels of familial correlations. We applied our new approach to 498 BRCA1 mutation carrier families recruited through the Breast Cancer Family Registry. Our results demonstrate the importance of the functional form of the time-varying covariate effect when assessing the role of risk-reducing salpingo oophorectomy on breast cancer. In particular, under the best fitting time-varying covariate model, the overall effect of risk-reducing salpingo oophorectomy on breast cancer risk was statistically significant in women with BRCA1 mutation.
Notes1477-0334 Choi, Yun-Hee Orcid: 0000-0002-5533-509x Jung, Hae Buys, Saundra Daly, Mary John, Esther M Hopper, John Andrulis, Irene Terry, Mary Beth Briollais, Laurent Orcid: 0000-0001-5741-9812 Journal Article England Stat Methods Med Res. 2021 Jul 7:9622802211008945. doi: 10.1177/09622802211008945.