Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations

Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations | 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 Article Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations Joseph Biggs, Joseph Challenger, Dominic Dee, Eldo Elobolobo, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5683637/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Cluster randomised trials (CRTs) are important tools for evaluating the community-wide effect of malaria interventions. During the design stage, CRT sample sizes need to be inflated to account for the cluster-heterogeneity in measured outcomes. One such measure of heterogeneity, the coefficient of variation ( k ), is typically used in malaria CRTs yet is often estimated without prior data. Underestimation of k undermines study power and increases the probability of CRTs generating null results. We conducted a meta-analysis of cluster-summary data from 24 malaria CRTs and calculated true k values for prevalence and incidence outcomes using methods-of-moments and regression modelling approaches. Using random effects regression modelling we investigated the impact of empirical k values on original trial power, effect size uncertainty and explored associated factors. Results revealed empirical estimates of k often exceeded those used in sample size calculations which heavily contributed to compromised study power and effect size precision. Increased between-cluster heterogeneity of outcomes was associated with outcome measures (i.e. incidence or prevalence), lower endemicity, seasonality of surveys and uneven intervention coverage across clusters. Study findings can be used to inform future malaria CRT sample size calculations and trial design to help ensure malaria interventions are effectively and feasibly evaluated. Health sciences/Diseases/Infectious diseases/Malaria Health sciences/Medical research/Epidemiology Full Text Additional Declarations There is NO Competing Interest. Ethics Statement This study is a meta-analysis of previously published research and does not involve any new data collection from human participants or animals. All data used in this study were obtained from publicly available sources or previously published studies that had received appropriate ethical approvals. Therefore, additional ethical approval was not required for this research. Supplementary Files Hetmal1paper2SuppInfov7.docx Supplementary Information Kestimationbyarm.txt k estimation do file Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2025 Read the published version in Nature Communications → 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-5683637","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":402614047,"identity":"416bf715-6f72-4a22-a88b-af0dbb69cc6e","order_by":0,"name":"Joseph 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All data used in this study were obtained from publicly available sources or previously published studies that had received appropriate ethical approvals. Therefore, additional ethical approval was not required for this research.\u003c/p\u003e","formattedTitle":"Characterisation of between-cluster heterogeneity in malaria cluster randomised trials to inform future sample size calculations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5683637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5683637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCluster randomised trials (CRTs) are important tools for evaluating the community-wide effect of malaria interventions. During the design stage, CRT sample sizes need to be inflated to account for the cluster-heterogeneity in measured outcomes. One such measure of heterogeneity, the coefficient of variation (\u003cem\u003ek\u003c/em\u003e), is typically used in malaria CRTs yet is often estimated without prior data. Underestimation of \u003cem\u003ek\u003c/em\u003e undermines study power and increases the probability of CRTs generating null results. We conducted a meta-analysis of cluster-summary data from 24 malaria CRTs and calculated true \u003cem\u003ek\u003c/em\u003e values for prevalence and incidence outcomes using methods-of-moments and regression modelling approaches. Using random effects regression modelling we investigated the impact of empirical \u003cem\u003ek\u003c/em\u003e values on original trial power, effect size uncertainty and explored associated factors. 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