{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/functional-linear-cox-regression-model-with","title":"Functional Linear Cox Regression Model with Frailty","arxiv_id":"2501.07450","date":"2025-01-13","proceeding":null,"authors":["Deniz Inan","Ufuk Beyaztas","Carmen D. Tekwe","Xiwei Chen","Roger S. Zoh"],"abstract":"This paper presents a functional linear Cox regression model with frailty to tackle unobserved heterogeneity in survival data with functional covariates. While traditional Cox models are common, they struggle to incorporate frailty effects that represent individual differences not captured by observed covariates. Our model combines scalar and functional covariates with a frailty term to address these unmeasured influences, creating a robust framework for high-dimensional survival analysis. We estimate parameters using functional principal component analysis and apply penalized partial likelihood for the frailty structure. A simulation study shows that our model outperforms traditional approaches in estimation accuracy and predictive capacity, especially with high frailty. We also analyze data from the National Health and Nutrition Examination Survey, highlighting significant links between physical activity and mortality in frail subpopulations. Our findings demonstrate the model's effectiveness in managing complex survival data, with potential applications in biomedical research related to unobserved heterogeneity. The method is available as an R package.","url_abs":"https://arxiv.org/abs/2501.07450v1","url_pdf":"https://arxiv.org/pdf/2501.07450v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"functional-linear-cox-regression-model-with","repo_url":"https://github.com/denizlukusluinan/fcoxfr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}