Protocol Complexity and Trial Failure: Predictive Modeling for Early Feasibility Assessment in Drug and Device Development

Protocol Complexity and Trial Failure: Predictive Modeling for Early Feasibility Assessment in Drug and Device Development | 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 Complexity and Trial Failure: Predictive Modeling for Early Feasibility Assessment in Drug and Device Development Francis Osei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7218992/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 Background: Unplanned discontinuation of clinical trials can delay medical product development, waste resources, and reduce confidence in evidence generation. Many terminations result from design or feasibility issues that could be addressed earlier in the planning process. Purpose: To determine whether structured protocol features available at trial registration can predict early trial discontinuation across drug and device studies. Methods: We analyzed 40,677 interventional trials registered on ClinicalTrials.gov between 2015 and 2025. Using structured protocol metadata, we applied Random Survival Forests and penalized Cox regression to model time to discontinuation. Concordance indices ($C$-index) were used to assess model performance, with subgroup analyses by sponsor type, intervention class, and design features. Results: The Random Survival Forest model outperformed other approaches, identifying clear predictors of early discontinuation. Trials with longer eligibility criteria, more exclusion conditions, and higher site counts showed increased risk of failure. Results were consistent across both drug and device categories. Conclusion: Structured protocol data can support early feasibility screening by identifying trials at greater risk of early termination. This approach may assist sponsors and regulatory stakeholders in improving trial planning and reducing preventable failures. 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-7218992","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505383672,"identity":"5c712f26-84cb-422b-b167-79a303fadf0a","order_by":0,"name":"Francis Osei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBAC+wMg0oBBzoABwiAMYCqNSdXCwJC4gWiHGUgffvbgR0Fd+nb2M4YPfxTY5TGwHz6KV789X5q5YY/B4dydPTnGBhIGycUMPGlpN/DawsNgJs1gcCB3w4EcMwkDA+bEBgkeMwJa2L8BtdSlG5x/Y/4jwaCeGC08IFuYEwxu5JgxHDA4TJSWMkmgXwx3znhWLNlgcDyxjbBf2LdJ/PhTJ2/On7zx448/1Yn97IeP4dWCBDggccRGpHIQYH9AguJRMApGwSgYSQAA7lhD118ZobQAAAAASUVORK5CYII=","orcid":"","institution":"Bayezian Limited","correspondingAuthor":true,"prefix":"","firstName":"Francis","middleName":"","lastName":"Osei","suffix":""}],"badges":[],"createdAt":"2025-07-26 06:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7218992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7218992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91476403,"identity":"bbac9dcb-8b8e-4ae5-ae53-c94d4751964b","added_by":"auto","created_at":"2025-09-17 01:16:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893543,"visible":true,"origin":"","legend":"","description":"","filename":"ProtocolLevelPredictorsofClinicalTrialDiscontinuation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7218992/v1_covered_2df1a511-3aad-40d6-9722-ad502f810adc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Protocol Complexity and Trial Failure: Predictive Modeling for Early Feasibility Assessment in Drug and Device Development","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-7218992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7218992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Unplanned discontinuation of clinical trials can delay medical product development, waste resources, and reduce confidence in evidence generation. Many terminations result from design or feasibility issues that could be addressed earlier in the planning process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To determine whether structured protocol features available at trial registration can predict early trial discontinuation across drug and device studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed 40,677 interventional trials registered on ClinicalTrials.gov between 2015 and 2025. Using structured protocol metadata, we applied Random Survival Forests and penalized Cox regression to model time to discontinuation. Concordance indices ($C$-index) were used to assess model performance, with subgroup analyses by sponsor type, intervention class, and design features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The Random Survival Forest model outperformed other approaches, identifying clear predictors of early discontinuation. Trials with longer eligibility criteria, more exclusion conditions, and higher site counts showed increased risk of failure. Results were consistent across both drug and device categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Structured protocol data can support early feasibility screening by identifying trials at greater risk of early termination. 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