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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.16350 |
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| _version_ | 1866916912127016960 |
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| author | Vinattieri, Maria Veronica Bonetti, Marco Czene, Kamila |
| author_facet | Vinattieri, Maria Veronica Bonetti, Marco Czene, Kamila |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multivariate Shared Frailty Cure-Rate models: a focus on Breast Cancer family history Vinattieri, Maria Veronica Bonetti, Marco Czene, Kamila Applications 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. |
| title | Multivariate Shared Frailty Cure-Rate models: a focus on Breast Cancer family history |
| topic | Applications |
| url | https://arxiv.org/abs/2508.16350 |