Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial

Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial | 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 Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial Lu Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5836301/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2025 Read the published version in BMC Medical Research Methodology → Version 1 posted 10 You are reading this latest preprint version Abstract Background: The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors.This gap necessitates a robust variable selection method tailored to the win ratio framework. Methods: We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining performance metrics via a generalized concordance index. The procedures are implemented in the wrnet R-package, publicly available at https://lmaowisc.github.io/wrnet/ . Results: Simulation studies demonstrate that wrnet outperforms traditional (regularized) Cox regression for time-to-first-event analysis, particularly in scenarios with differing covariate effects on mortality and nonfatal events. When applied to data from the HF-ACTION trial, the method identified prognostic variables and achieved superior predictive accuracy compared to regularized Cox models, as measured by overall and component-specific concordance indices. Conclusion: The wrnet approach combines the interpretability and clinical relevance of the win ratio with the scalability and robustness of elastic net regularization. The accompanying R-package provides a user-friendly interface for routine application of the procedures, whenever appropriate. Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects. Full Text Additional Declarations No competing interests reported. Supplementary Files supp.pdf Rcodetoreproduceresults.zip Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2025 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 10 Mar, 2025 Reviews received at journal 09 Mar, 2025 Reviews received at journal 16 Feb, 2025 Reviewers agreed at journal 14 Feb, 2025 Reviewers agreed at journal 27 Jan, 2025 Reviewers invited by journal 24 Jan, 2025 Editor invited by journal 21 Jan, 2025 Editor assigned by journal 21 Jan, 2025 Submission checks completed at journal 21 Jan, 2025 First submitted to journal 15 Jan, 2025 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-5836301","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410711204,"identity":"a8b3a1aa-6591-45b2-8d47-80facf9140e4","order_by":0,"name":"Lu Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPhDxsUECxpdAInEANiBmnAnSwsbA2EC0FmbeBgaYFmS7cGkRO/zwse0OCzn5+c3PHzC2WdjzNzAfvM2DT4t0mrFx7hkJY4NjbIYNjG0SiTMOsCVb49eSYCadC1S5gY0BqGWbRIIBA4+ZNH4t6d+kLYFa5rexfwRpsTdg4P9GQEuOmTTIPQ3HeMC2MG5g4GEjpKXYsBfsl5zCGYn/gH45zGZsOQePFn7p9I0Pfu6ok5NvPr7hw4czdfb87c0Pb7zBowUVJIAIZqKVj4JRMApGwSjABQDn5kCyeRGAuQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2025-01-15 17:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5836301/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5836301/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12874-025-02554-w","type":"published","date":"2025-04-17T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81050802,"identity":"99925cf2-8506-4026-b0dd-e70e50b3355e","added_by":"auto","created_at":"2025-04-21 16:05:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":493564,"visible":true,"origin":"","legend":"","description":"","filename":"wrnetlatexfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5836301/v1_covered_8087721c-148f-4fa4-aa34-842d261b512b.pdf"},{"id":75754516,"identity":"e8e570ae-0225-4e5a-af29-e81b265aaf0b","added_by":"auto","created_at":"2025-02-07 22:11:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":242331,"visible":true,"origin":"","legend":"","description":"","filename":"supp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5836301/v1/4a33e467c45da2235da03b1d.pdf"},{"id":75754517,"identity":"aeaf1d35-b211-4989-b52f-40b43362250f","added_by":"auto","created_at":"2025-02-07 22:11:15","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":343636,"visible":true,"origin":"","legend":"","description":"","filename":"Rcodetoreproduceresults.zip","url":"https://assets-eu.researchsquare.com/files/rs-5836301/v1/828fa4dee829b9e6b7154ced.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5836301/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5836301/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: The win ratio has been widely used in the analysis of hierarchical composite endpoints, which prioritize critical outcomes such as mortality over nonfatal, secondary events. Although a regression framework exists to incorporate covariates, it is limited to low-dimensional datasets and may struggle with numerous predictors.This gap necessitates a robust variable selection method tailored to the win ratio framework.\n\nMethods: We propose an elastic net-type regularization approach for win ratio regression, extending the proportional win-fractions (PW) model in low-dimensional settings. The method addresses key challenges, including adapting pairwise comparisons to penalized regression, optimizing model selection through subject-level cross-validation, and defining performance metrics via a generalized concordance index. 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Future research could explore additional applications or refine the methodology to address non-proportionalities in win-loss risks and nonlinearities in covariate effects.","manuscriptTitle":"Regularized win ratio regression for variable selection and risk prediction, with an application to a cardiovascular trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 22:11:10","doi":"10.21203/rs.3.rs-5836301/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-10T15:25:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-10T00:34:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-16T21:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248575180646608559821990819932328573461","date":"2025-02-15T00:48:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247785537481821282658751073316666657486","date":"2025-01-27T16:10:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-24T19:29:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-21T19:49:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-21T06:40:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-21T06:40:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-01-15T16:58:17+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"d45c7435-930b-43cc-b110-3a57154004a4","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T15:59:44+00:00","versionOfRecord":{"articleIdentity":"rs-5836301","link":"https://doi.org/10.1186/s12874-025-02554-w","journal":{"identity":"bmc-medical-research-methodology","isVorOnly":false,"title":"BMC Medical Research Methodology"},"publishedOn":"2025-04-17 15:57:17","publishedOnDateReadable":"April 17th, 2025"},"versionCreatedAt":"2025-02-07 22:11:10","video":"","vorDoi":"10.1186/s12874-025-02554-w","vorDoiUrl":"https://doi.org/10.1186/s12874-025-02554-w","workflowStages":[]},"version":"v1","identity":"rs-5836301","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5836301","identity":"rs-5836301","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}