Illustrating the Assumptions of Meta-Regression in Treatment Networks

Illustrating the Assumptions of Meta-Regression in Treatment Networks | 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 Illustrating the Assumptions of Meta-Regression in Treatment Networks Nana-adjoa Kwarteng, Theodoros Evrenoglou, Julia Mueller, Moritz Elsaesser, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8235913/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Network meta-analysis (NMA) is a common statistical method used to synthesize evidence across multiple studies, enabling simultaneous comparison of multiple competing treatments for a given condition. Network meta-regression (NMR) extends NMA by adjusting treatment effect estimates based on study-level characteristics, helping to explain residual heterogeneity. Despite its usefulness, NMR adoption as a technique has been limited due to conceptual complexity, implementation challenges, and limitations in sparse-data settings. NMR model coefficients follow independent, exchangeable, or common assumptions, each applied with or without enforcing consistency among treatment comparisons. However, choosing between these models is complex, as different modeling assumptions can lead to varying results and interpretations depending on factors like model fit and data availability. Additionally, before fitting NMR models without consistency, meta-analysts must also consider potential data directionality, where certain study characteristics (e.g., sponsorship) may systematically bias treatment effect estimates. Methods We present frequentist tools for NMR and evaluate the properties of four different modelling assumptions achievable via design matrix modifications to the standard NMA model. Based on these properties, we provide recommendations for NMR implementation that account for data availability and the research question of interest. We also offer guidance on interpreting treatment-by-covariate interactions in the context of underlying NMR assumptions. Results We illustrate the different NMR modeling assumptions using a network of ten diabetes treatments. Findings highlight the impact of data directionality in models without consistency in the treatment-by-covariate interactions. Differences across modeling assumptions underscore the importance of carefully considering both the network structure and the characteristics of included studies when implementing NMR. Conclusion This work elucidates crucial NMR assumptions and demonstrates the importance of the network structure for NMR. The introduced tools can streamline the implementation of NMR, facilitating the exploration of sources of heterogeneity and inconsistency in NMA and expanding tools available to researchers in the field of evidence synthesis. network meta-analysis network meta-regression small data directionality consistency assumption Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementTablesIllustratingAssumptionsMetaRegressionTreatmentNetworks10.xlsx SupplementIllustratingAssumptionsMetaRegressionTreatmentNetworks10.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviewers invited by journal 23 Dec, 2025 Editor invited by journal 02 Dec, 2025 Editor assigned by journal 30 Nov, 2025 Submission checks completed at journal 30 Nov, 2025 First submitted to journal 29 Nov, 2025 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-8235913","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576534664,"identity":"518bbf09-928c-45d5-996c-ff9c8c9ec5a0","order_by":0,"name":"Nana-adjoa 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