Protocol-Level Predictors of Clinical Trial Discontinuation: A Survival Analysis Using Structured Registry Metadata

Protocol-Level Predictors of Clinical Trial Discontinuation: A Survival Analysis Using Structured Registry Metadata | 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 Protocol-Level Predictors of Clinical Trial Discontinuation: A Survival Analysis Using Structured Registry Metadata Francis Osei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7013772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: To evaluate whether structured protocol features can predict early trial discontinuation and support feasibility assessment during clinical planning. Methods: We analyzed 40,677 interventional trials registered on ClinicalTrials.gov (2015–2025) using survival models, including Kaplan–Meier, Cox-Ridge, Cox-Lasso, parametric AFT models, and Random Survival Forests (RSF). Protocol-level features were extracted from registry metadata, and model performance was evaluated using the concordance index (C-index) with stratified subgroup analysis. Results: RSF achieved the highest test set C-index (0.6882), outperforming Cox-Ridge (0.6335) and parametric models. The RSF risk scores highlighted trial complexity markers such as eligibility length, site count, and sponsor type as strong predictors of early termination. Subgroup evaluations showed stable RSF performance across design and regulatory strata, while Cox-Ridge exhibited reduced discrimination in FDA-regulated and crossover trials. Conclusion: Structured protocol data can be used to estimate trial termination risk before launch. RSF models offer accurate, non-parametric prediction for risk-based planning, while Cox-Ridge provides interpretable baselines. These tools may aid trial sponsors and planners in feasibility screening and early decision-making. Clinical trial discontinuation Protocol metadata Survival analysis Random survival forests Risk prediction Trial design complexity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-7013772","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483138912,"identity":"f2f51886-5d5d-4931-a868-430c6eca5603","order_by":0,"name":"Francis Osei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBAC9gYQacAgZ8AAYRAGPAcgKo1J1cLAkLiBaIfxSB9+9uBHQV36dvYzhg9/FNjlMbAfPopXPw9fmrlhj8Hh3J09OcYGEgbJxQw8aWk38Gmx52Ewk2YwOJC74UCOmYSBAXNigwSPGV4tPDzs34Ba6tINzr8x/5FgUE+MFh6QLcwJBjdyzBgOGBwmSkuZJNAvhjtnPCuWbDA4nthGyC9Ah22T+PGnTt6cP3njxx9/qhP72Q8fw6sFCXBA4pGNSOUgwP6ABMWjYBSMglEwkgAA5YNDv29fyUQAAAAASUVORK5CYII=","orcid":"","institution":"Bayezian Limited","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"","lastName":"Osei","suffix":""}],"badges":[],"createdAt":"2025-06-30 19:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7013772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7013772/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87195610,"identity":"8c33ca42-161d-4ad2-9a45-a52294833188","added_by":"auto","created_at":"2025-07-21 12:17:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859581,"visible":true,"origin":"","legend":"","description":"","filename":"survivalAnalysisofTrials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7013772/v1_covered_93c8febf-ee34-460c-a148-d2873802d756.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protocol-Level Predictors of Clinical Trial Discontinuation: A Survival Analysis Using Structured Registry Metadata","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clinical trial discontinuation, Protocol metadata, Survival analysis, Random survival forests, Risk prediction, Trial design complexity","lastPublishedDoi":"10.21203/rs.3.rs-7013772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7013772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To evaluate whether structured protocol features can predict early trial discontinuation and support feasibility assessment during clinical planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed 40,677 interventional trials registered on ClinicalTrials.gov (2015–2025) using survival models, including Kaplan–Meier, Cox-Ridge, Cox-Lasso, parametric AFT models, and Random Survival Forests (RSF). 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