Covariate-constrained randomization in cluster randomized 2x2 factorial trials: Application to a diabetes prevention study

Covariate-constrained randomization in cluster randomized 2x2 factorial trials: Application to a diabetes prevention study | 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 Covariate-constrained randomization in cluster randomized 2x2 factorial trials: Application to a diabetes prevention study Juned Siddique, Zhehui Li, Matthew J. O’Brien This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3783684/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Sep, 2024 Read the published version in Trials → Version 1 posted 5 You are reading this latest preprint version Abstract Background: Cluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs. Methods: Motivated by the BEGIN study—a CRT for weight loss among patients with pre-diabetes—we develop methods for performing CR in 2x2 factorial cluster randomized trials. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, and analysis strategies. Results: Compared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small. Conclusions: CR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and Type 1 error rates at their nominal levels. CRT balance confounding Full Text Cite Share Download PDF Status: Published Journal Publication published 06 Sep, 2024 Read the published version in Trials → Version 1 posted Editorial decision: Major revision 15 Apr, 2024 Reviewers agreed at journal 27 Mar, 2024 Reviewers invited by journal 21 Mar, 2024 Editor assigned by journal 02 Feb, 2024 First submitted to journal 19 Dec, 2023 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3783684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282178397,"identity":"a6970af5-3149-4ed9-b9fa-80d7d0362c4f","order_by":0,"name":"Juned Siddique","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACZgYDiQQgzQ/iMDYgJJDZ2LVIglQcBBFshLQwALWAyQPEapFvZ95440HZYTnj283HPn/ccVhOfj6QUcBgI7vhAA4rDrMVWyScO2xsdudY8oyDZw4bGxxjS549gyHNGKcWZh4zicS2w4nbbuQYMxxsu524gY3HmJmH4XAiLi3yzRAt9ZtnQLTUz2/j/wzU8h+nFobDEC0JBhIQLQkMx3iYgVoO4NQC9Uu64YwbackMZ9v+G244lgZ0mEGy8UxcDus/vPHmjzJref4ZyYcZKtvS5OWbDz9m5qmwk+3D5TAwYMO0HZ9y7FpGwSgYBaNgFCAAAPSkX7jozPP2AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1501-4152","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Juned","middleName":"","lastName":"Siddique","suffix":""},{"id":282178398,"identity":"1c53419b-a849-462d-b2d4-b9084d8c6118","order_by":1,"name":"Zhehui Li","email":"","orcid":"","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhehui","middleName":"","lastName":"Li","suffix":""},{"id":282178399,"identity":"ae2a158c-f12b-4889-883c-6b95069efc02","order_by":2,"name":"Matthew J. 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When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Motivated by the BEGIN study—a CRT for weight loss among patients with pre-diabetes—we develop methods for performing CR in 2x2 factorial cluster randomized trials. 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