On Power Calculation Based on Effect Size in Clinical Research

On Power Calculation Based on Effect Size in Clinical Research | 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 On Power Calculation Based on Effect Size in Clinical Research Yinuo Zhang, Shein-Chung Chow This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3895270/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 Power analysis for sample size calculation (power calculation) plays an important role in clinical research to guarantee that we have sufficient power for detecting a clinically meaningful difference (treatment effect) at a pre-specified level of significance. In practice, however, there may be little or no information regarding the test treatment under study available. In this case, it is suggested that power calculation for detecting an anticipated effect size adjusted for standard deviation be performed. In practice, power calculation based on effect size is commonly considered for a quick assessment of sample size requirement. It reduces a two-parameter problem into a single parameter problem by taking both mean response and variability into consideration. However, this approach has been criticized that the resultant sample size may not guarantee that final clinical results are reproducible if the variability is large. In addition, for a fixed effect size, study endpoints of different data types cannot translate one another in terms of clinically meaningful differences. This article meant to provide a comprehensive summarization of the relationship between power calculation based on effect size and based on treatment effect in terms of different study endpoints of different data types. Treatment effect Effect size Power analysis Sample size calculation Hypothesis testing Full Text 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. 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-3895270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272190555,"identity":"04ff2eb5-58e9-4ecd-96d0-da4e035c29f2","order_by":0,"name":"Yinuo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFAC5sMPPlQwM0iA2DzEaWFLM5xxhjQtPAbSvG2kaDE4fyzBcOY868SZ7Q2MD962EaPlwOEDDz5uS0+czXOA2XAuUVoOtgFt2XY4cZ5EAhvQhcRoOQzyyxygFvkH7L+J03IMpKXhcOJsCQY2ZqK0SJ4BBfKxdOOZPYnNknPOEaGF7/xhYFTWWMvOOH744Ic3ZURoUTgAZzI2EKEeCOSJVDcKRsEoGAUjGQAAL2g7KSpBfzwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0000-9393-2330","institution":"Duke University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yinuo","middleName":"","lastName":"Zhang","suffix":""},{"id":272190556,"identity":"af6af7cf-a23c-4424-825e-b35ae043be11","order_by":1,"name":"Shein-Chung Chow","email":"","orcid":"","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Shein-Chung","middleName":"","lastName":"Chow","suffix":""}],"badges":[],"createdAt":"2024-01-24 21:47:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3895270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3895270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52924930,"identity":"536bb75b-168a-4da4-bf5c-8e164702671c","added_by":"auto","created_at":"2024-03-18 17:57:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":629072,"visible":true,"origin":"","legend":"","description":"","filename":"OnPowerCalculationBasedon.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3895270/v1_covered_d13ae256-9681-4dc2-a406-f3a572b80d45.pdf"}],"financialInterests":"","formattedTitle":"On Power Calculation Based on Effect Size in Clinical Research","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":"Treatment effect, Effect size, Power analysis, Sample size calculation, Hypothesis testing","lastPublishedDoi":"10.21203/rs.3.rs-3895270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3895270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePower analysis for sample size calculation (power calculation) plays an important role in clinical research to guarantee that we have sufficient power for detecting a clinically meaningful difference (treatment effect) at a pre-specified level of significance. 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