Multivariate Shared Frailty Cure-Rate models: a focus on Breast Cancer family history

Abstract: We discuss a shift in perspective from traditional approaches to breast cancer risk prediction: modelling families rather than individuals as unit of analysis. By investigating the latent familial risk underlying breast cancer diagnoses, we introduce a Multivariate Shared Frailty Cure-Rate model. This model captures the familial risk as a shared frailty among members and explicitly accounts for a fraction of women not susceptible to breast cancer. We aim at identifying the high-risk families to better target screening and prevention, ultimately improving early detection. A comparative analysis with Cox models and univariate models - where a binary risk indicator acts as best guess for the latent high-risk group - is conducted using simulation studies and data from the Swedish Multi-Generational Breast Cancer registry. We demonstrate the critical importance of using complete family history of breast cancer to accurately identify high-risk families and show that the Multivariate Shared Frailty Cure-Rate model, capturing both the fraction of non-susceptible subjects and the survival distribution among susceptibles, enhances explanatory power, improves prediction accuracy, and offers a broader representation of the disease process.