Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods

Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods | 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 Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods Lei Yan, Ziji Yu, Liwen Wu, Rachael Liu, Jianchang Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5374972/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2025 Read the published version in Therapeutic Innovation & Regulatory Science → Version 1 posted 9 You are reading this latest preprint version Abstract The traditional clinical trial monitoring process, which relies heavily on site visits and manual review of accumulative patient data reported through Electronic Data Capture system, is time-consuming and resource-intensive. The recently emerged risk-based monitoring (RBM) and quality tolerance limit (QTL) framework offers a more efficient alternative solution to traditional SDV (source data verification) based quality assurance. These frameworks aim at proactively identifying systematic issues that impact patient safety and data integrity. In this paper, we proposed a machine learning enabled approach to facilitate real-time, automated monitoring of clinical trial QTL risk assessment. Unlike the traditional quality assurance process, where QTLs are evaluated based on single-source data and arbitrary defined fixed threshold, we utilize the QTL-ML framework to integrate information from multiple clinical domains to predict the clinical QTL of variety types at program, study, site and patient level. Moreover, our approach is assumption-free, relying not on historical expectations but on dynamically accumulating trial data to predict quality tolerance limit risks in an automated manner. Embedded within ICH-E6 recommended RBM principles, this innovative machine learning solution for QTL monitoring has the potential to transform sponsors’ ability to protect patient safety, reduce trial duration, and lower trial costs. good clinical practice risk-based monitoring quality tolerance limits machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files supplement.docx Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2025 Read the published version in Therapeutic Innovation & Regulatory Science → Version 1 posted Editorial decision: Revision requested 03 Jan, 2025 Reviews received at journal 03 Jan, 2025 Reviews received at journal 20 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviewers agreed at journal 29 Nov, 2024 Reviewers invited by journal 28 Nov, 2024 Editor assigned by journal 08 Nov, 2024 Submission checks completed at journal 02 Nov, 2024 First submitted to journal 01 Nov, 2024 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-5374972","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375810611,"identity":"96b01268-2f58-437c-9e69-f6d1af5ebc9f","order_by":0,"name":"Lei Yan","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yan","suffix":""},{"id":375810612,"identity":"1c156e48-0ddd-46c8-8524-268f5f3f73b0","order_by":1,"name":"Ziji 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