{"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/boosting-joint-models-for-longitudinal-and","title":"Boosting Joint Models for Longitudinal and Time-to-Event Data","arxiv_id":"1609.02686","date":"2016-09-09","proceeding":null,"authors":["Elisabeth Waldmann","David Taylor-Robinson","Nadja Klein","Thomas Kneib","Tania Pressler","Matthias Schmid","Andreas Mayr"],"abstract":"Joint Models for longitudinal and time-to-event data have gained a lot of\nattention in the last few years as they are a helpful technique to approach\ncommon a data structure in clinical studies where longitudinal outcomes are\nrecorded alongside event times. Those two processes are often linked and the\ntwo outcomes should thus be modeled jointly in order to prevent the potential\nbias introduced by independent modelling. Commonly, joint models are estimated\nin likelihood based expectation maximization or Bayesian approaches using\nframeworks where variable selection is problematic and which do not immediately\nwork for high-dimensional data. In this paper, we propose a boosting algorithm\ntackling these challenges by being able to simultaneously estimate predictors\nfor joint models and automatically select the most influential variables even\nin high-dimensional data situations. We analyse the performance of the new\nalgorithm in a simulation study and apply it to the Danish cystic fibrosis\nregistry which collects longitudinal lung function data on patients with cystic\nfibrosis together with data regarding the onset of pulmonary infections. This\nis the first approach to combine state-of-the art algorithms from the field of\nmachine-learning with the model class of joint models, providing a fully\ndata-driven mechanism to select variables and predictor effects in a unified\nframework of boosting joint models.","url_abs":"http://arxiv.org/abs/1609.02686v2","url_pdf":"http://arxiv.org/pdf/1609.02686v2.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":"boosting-joint-models-for-longitudinal-and","repo_url":"https://github.com/mayrandy/JMboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"variable-selection","task_name":"Variable Selection"}],"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}