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A general class of shared gamma frailty models for risk prediction, with application to breast cancer screening adherence

  • Marco Bonetti
  • Edoardo Ratti
  • Solveig Hofvind
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Abstract

We introduce a new class of mixed models for longitudinal or clustered binary data. The formulation is inspired by the shared frailty multivariate survival model for clustered data, and it allows for closed forms for all the relevant quantities, which allows for an efficient and fast computational solution of the associated inferential and prediction problems. We focus on an easily interpretable specification of the model, discuss the associated individual risk prediction, and compare our results to the traditional GLMM model for binary data with random intercept. We implement the methods on a large motivating dataset on the individual longitudinal adherence to the breast cancer screening program of the Oslo region.

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Cite

BibTeXRIS
BibTeX
@misc{bonetti2026general,
  title = {{A general class of shared gamma frailty models for risk prediction, with application to breast cancer screening adherence}},
  author = {Marco Bonetti and Edoardo Ratti and Solveig Hofvind},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.28548},
  eprint = {2610.28548},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.28548},
}
RIS
TY  - UNPB
TI  - A general class of shared gamma frailty models for risk prediction, with application to breast cancer screening adherence
AU  - Marco Bonetti
AU  - Edoardo Ratti
AU  - Solveig Hofvind
PY  - 2026
DA  - 2026/10/20
PB  - arXiv
DO  - 10.48550/arxiv.2610.28548
UR  - https://arxiv.org/abs/2610.28548
AB  - We introduce a new class of mixed models for longitudinal or clustered binary data. The formulation is inspired by the shared frailty multivariate survival model for clustered data, and it allows for closed forms for all the relevant quantities, which allows for an efficient and fast computational solution of the associated inferential and prediction problems. We focus on an easily interpretable specification of the model, discuss the associated individual risk prediction, and compare our results to the traditional GLMM model for binary data with random intercept. We implement the methods on a large motivating dataset on the individual longitudinal adherence to the breast cancer screening program of the Oslo region.
ER  -