JMbdirect: Joint Modeling with Semi-Parametric Link Functions for Bidirectional Feedback in Longitudinal-Survival Data

JMbdirect: Joint Modeling with Semi-Parametric Link Functions for Bidirectional Feedback in Longitudinal-Survival Data | 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 JMbdirect: Joint Modeling with Semi-Parametric Link Functions for Bidirectional Feedback in Longitudinal-Survival Data Atanu Bhattacharjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9013474/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Joint models of longitudinal and time-to-event data are essential for dynamic prediction in chronic disease research, but standard formulations typically assume linear biomarker–hazard associations and unidirectional influence from biomarkers to events. These assumptions are often violated in multimorbidity and oncology, where events alter subsequent biomarker trajectories and relationships are non-linear or threshold-like. We extend the joint modeling framework through the JMbdirect package by introducing semi-parametric association structures based on penalized splines and neural additive components, and by embedding explicit bidirectional feedback between events and biomarkers. Estimation is supported via penalized likelihood with Laplace approximation and a Bayesian alternative using Hamiltonian Monte Carlo. Simulation studies demonstrate that incorporating feedback and flexible links improves discrimination and calibration compared with classical linear specifications, particularly under non-linear risk mechanisms. Applications in oncology (locoregional control, progression-free survival, overall survival) and diabetes–cardiovascular multimorbidity illustrate how these methods provide more accurate and interpretable dynamic predictions. By combining methodological innovation with a publicly available dashboard interface, this work advances the translation of joint modeling into personalized risk prediction and clinical decision support. joint modelling bidirectional feedback semi-parametric links dynamic prediction electronic health records Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 04 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 02 Mar, 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-9013474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625167538,"identity":"89ac8dd4-002d-4d73-bfb4-8feb95eb773f","order_by":0,"name":"Atanu Bhattacharjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFAD9gYgYWBBhEo2MGnAwMNzAERLkKJFIgHEIEIL//zmZ495/vyRt5d8fnXDjwIJBv727gS8WiSOsZkb87YZGPZI55Td7AE6TOLM2Q34rTnGYCbN22DACNSSdoMHqMVAIhe/Fvlj7N+kef4Y2PdInkm7+YcYLQbHeMykedgMEnsk2I/dJsoWw2M5ZZJz24yTe87ksN2WMZDgIegXucPHt0m8+SNn295+/NnNN39s5Pjbewl4HwF4DMAkscpBgP0BKapHwSgYBaNgBAEA6HtBLMp9HnYAAAAASUVORK5CYII=","orcid":"","institution":"University of Dundee","correspondingAuthor":true,"prefix":"","firstName":"Atanu","middleName":"","lastName":"Bhattacharjee","suffix":""}],"badges":[],"createdAt":"2026-03-02 20:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9013474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9013474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107489997,"identity":"fa0d9814-9cbc-49f6-ace0-70230d32f8a5","added_by":"auto","created_at":"2026-04-22 02:49:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1406476,"visible":true,"origin":"","legend":"","description":"","filename":"JMbdirect2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9013474/v1_covered_7f5cbfbb-919c-4813-947e-917e19ed8825.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"JMbdirect: Joint Modeling with Semi-Parametric Link Functions for Bidirectional Feedback in Longitudinal-Survival Data","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":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"joint modelling, bidirectional feedback, semi-parametric links, dynamic prediction, electronic health records","lastPublishedDoi":"10.21203/rs.3.rs-9013474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9013474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJoint models of longitudinal and time-to-event data are essential for dynamic prediction in chronic disease research, but standard formulations typically assume linear biomarker–hazard associations and unidirectional influence from biomarkers to events. 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