{"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/modeling-recovery-curves-with-application-to","title":"Modeling Recovery Curves With Application to Prostatectomy","arxiv_id":"1504.06964","date":"2015-04-27","proceeding":null,"authors":["Fulton Wang","Tyler H. McCormick","Cynthia Rudin","John Gore"],"abstract":"We propose a Bayesian model that predicts recovery curves based on\ninformation available before the disruptive event. A recovery curve of interest\nis the quantified sexual function of prostate cancer patients after\nprostatectomy surgery. We illustrate the utility of our model as a\npre-treatment medical decision aid, producing personalized predictions that are\nboth interpretable and accurate. We uncover covariate relationships that agree\nwith and supplement that in existing medical literature.","url_abs":"http://arxiv.org/abs/1504.06964v6","url_pdf":"http://arxiv.org/pdf/1504.06964v6.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":"modeling-recovery-curves-with-application-to","repo_url":"https://github.com/fultonwang/recovery_curve","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}