{"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/c-mix-a-high-dimensional-mixture-model-for","title":"C-mix: a high dimensional mixture model for censored durations, with applications to genetic data","arxiv_id":"1610.07407","date":"2016-10-24","proceeding":null,"authors":["Simon Bussy","Agathe Guilloux","Stéphane Gaïffas","Anne-Sophie Jannot"],"abstract":"We introduce a mixture model for censored durations (C-mix), and develop\nmaximum likelihood inference for the joint estimation of the time distributions\nand latent regression parameters of the model. We consider a high-dimensional\nsetting, with datasets containing a large number of biomedical covariates. We\ntherefore penalize the negative log-likelihood by the Elastic-Net, which leads\nto a sparse parameterization of the model. Inference is achieved using an\nefficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which we\nprovide convergence properties. We then propose a score by assessing the\npatients risk of early adverse event. The statistical performance of the method\nis examined on an extensive Monte Carlo simulation study, and finally\nillustrated on three genetic datasets with high-dimensional covariates. We show\nthat our approach outperforms the state-of-the-art, namely both the CURE and\nCox proportional hazards models for this task, both in terms of C-index and\nAUC(t).","url_abs":"http://arxiv.org/abs/1610.07407v5","url_pdf":"http://arxiv.org/pdf/1610.07407v5.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":"abstracts"},"code_links":[{"paper_slug":"c-mix-a-high-dimensional-mixture-model-for","repo_url":"https://github.com/SimonBussy/C-mix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}