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

TL;DR

This paper introduces a multivariate shared frailty cure-rate model for breast cancer risk prediction that considers family history, improving identification of high-risk families and enhancing predictive accuracy over traditional models.

Contribution

It presents a novel multivariate shared frailty cure-rate model that explicitly accounts for familial risk and non-susceptible individuals, advancing breast cancer risk modeling.

Findings

Complete family history improves risk identification.

The proposed model outperforms Cox and univariate models.

It better captures the disease process and enhances prediction.

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.