Bayesian Semiparametric Mixture Cure (Frailty) Models

Bayesian Semiparametric Mixture Cure (Frailty) Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bayesian Semiparametric Mixture Cure (Frailty) Models Fatih Kızılaslan, Valeria Vitelli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8925470/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract In recent years, mixture cure models have gained increasing popularity in survival analysis as an alternative to the Cox proportional hazards model, particularly in settings where a subset of patients is considered cured. The proportional hazards mixture cure model is especially advantageous when the presence of a cured fraction can be reasonably assumed, providing a more accurate representation of long-term survival dynamics. In this study, we propose a novel hierarchical Bayesian framework for the semiparametric mixture cure model, which accommodates both the inclusion and exclusion of a frailty component, allowing for greater flexibility in capturing unobserved heterogeneity among patients.Samples from the posterior distribution are obtained using a Markov chain Monte Carlo method, leveraging a hierarchical structure inspired by Bayesian Lasso. Comprehensive simulation studies are conducted across diverse scenarios to evaluate the performance and robustness of the proposed models. Bayesian model comparison and assessment are performed using various criteria. Finally, the proposed approaches are applied to two well-known datasets in the cure model literature: the E1690 melanoma trial and a colon cancer clinical trial. Bayesian inference MCMC method mixture cure model piecewise exponential distribution R Stan semiparametric model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 21 Feb, 2026 Submission checks completed at journal 21 Feb, 2026 First submitted to journal 20 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8925470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595542943,"identity":"cb4d894b-8e00-4c0e-b148-169992f1b951","order_by":0,"name":"Fatih Kızılaslan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBAC9h7GBoaEAgYGPgYGxgcMDAcIa+E5A9JiwMDAxsDAbECkFhAJ0cImQZwWnsPNHx4Y2NmzsZ89Vs1Tc0eOn4H54aMb+LTwNjYYJBgkJ7bx5KXd5jn2zFiygc3YOAePFnt+xoaEBAPmBDaGHLPbPGyHEzcc4GGTxqeFB6jlQIJBvT0b/xuzYp5/xGjhbWxsSDA4zNgmkWPGzNtGjBaeg83AQD6e2Cbxxlhybt9hY8lmAn7h4Ul//PFHRbU9P3+O4Yc33w7L8bM3P3yMTwsKYOIBkczEKgcBxh+kqB4Fo2AUjIIRAwCHz0dgoXsmqwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Oslo","correspondingAuthor":true,"prefix":"","firstName":"Fatih","middleName":"","lastName":"Kızılaslan","suffix":""},{"id":595542945,"identity":"db3e38df-6843-42bb-b9bb-b7a86dd7b309","order_by":1,"name":"Valeria Vitelli","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Vitelli","suffix":""}],"badges":[],"createdAt":"2026-02-20 11:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8925470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8925470/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103507280,"identity":"7f7b42ed-805f-420d-916c-ac1fca7fa4cc","added_by":"auto","created_at":"2026-02-26 13:40:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":574830,"visible":true,"origin":"","legend":"","description":"","filename":"SMCMSpringertemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8925470/v1_covered_0072811a-8486-406e-ab59-da102c06cadf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian Semiparametric Mixture Cure (Frailty) Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"statistics-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"stco","sideBox":"Learn more about [Statistics and Computing](http://link.springer.com/journal/11222)","snPcode":"11222","submissionUrl":"https://submission.nature.com/new-submission/11222/3","title":"Statistics and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bayesian inference, MCMC method, mixture cure model, piecewise exponential distribution, R Stan, semiparametric model","lastPublishedDoi":"10.21203/rs.3.rs-8925470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8925470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, mixture cure models have gained increasing popularity in survival analysis as an alternative to the Cox proportional hazards model, particularly in settings where a subset of patients is considered cured. 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