A Competing Risks Model with Binary Time Varying Covariates for Estimation of Breast Cancer Risks in BRCA1 Families
Yun-Hee Choi, Hae Jung, Saundra Buys, Mary Daly, Esther John, John, Hopper, Irene Andrulis, Mary-Beth Terry, Laurent Briollais

TL;DR
This paper develops a sophisticated statistical model to accurately estimate breast and ovarian cancer risks in BRCA1 families, accounting for time-varying factors, competing risks, and family selection biases, improving risk assessment precision.
Contribution
It introduces a new correlated competing risks model with flexible time-varying covariates and an ascertainment correction method for familial breast cancer risk estimation.
Findings
RRSO significantly reduces breast cancer risk in BRCA1 mutation carriers.
The model accurately estimates cause-specific penetrances and familial correlations.
Flexible covariate modeling improves risk assessment accuracy.
Abstract
Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy (RRSO) 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 (TVCs). Different parametric forms for these TVCs are proposed for more flexibility and a correlated gamma frailty model is specified to account for the correlated competing events. We also introduced 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…
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Taxonomy
TopicsBRCA gene mutations in cancer · Genetic Associations and Epidemiology · Genetic factors in colorectal cancer
