Choosing among anchored indirect comparison methods in health technology assessment: simulation evidence and a practical decision framework

Choosing among anchored indirect comparison methods in health technology assessment: simulation evidence and a practical decision framework | 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 Choosing among anchored indirect comparison methods in health technology assessment: simulation evidence and a practical decision framework Lingyao Sun, Dachuang Zhou, Lei Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7824621/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Decision makers in health technology assessment rely on network meta-analysis to synthesize incomplete head to head evidence, yet imbalances in effect modifiers can bias transported relative effects. Adjustment options include study-level and multilevel meta-regression, matching-adjusted indirect comparison, simulated treatment comparison, and network meta-interpolation. Comparative performance under graded violations of shared effect modification and practical guidance that links method choice to testable assumptions remain limited. Methods We built a four-trial anchored network and defined Trial 3 as the target population. Binary outcomes were generated on the probit scale with two covariates. We examined three scenarios for shared effect modification, namely shared, stronger interactions for treatment C by one half, and opposite directions. Each scenario had fifty replications. All methods estimated B versus C in the Trial 3 population on the probit scale. Performance metrics were bias, root mean squared error, and empirical coverage of the nominal ninety-five percent interval. Results ML-NMR showed the smallest bias and coverage closest to the nominal level across scenarios. Study-level NMR maintained reasonable precision when shared effect modification held but showed wider intervals and higher bias when interactions differed. MAIC and STC were nearly unbiased when shared effect modification was valid and key modifiers were correctly identified, but both developed bias and undercoverage when interaction strength or direction diverged. NMI was comparatively stable, with reliability dependent on covariance stability. Conventional NMA performed worst under pronounced covariate mismatch. Conclusions Analysts should first test prerequisite assumptions, including shared effect modification and homoscedasticity when relevant. Aligning method choice with verified assumptions reduces misspecification risk in health technology assessment and supports transparent, credible decisions. Simulation study Indirect comparison Shared effect modification Health technology assessment Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 16 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor invited by journal 20 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 10 Oct, 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. We do this by developing innovative software and high quality services for the global research community. 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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-7824621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549216801,"identity":"0902d9a9-bfd5-4acc-8f71-f59a1e3353e1","order_by":0,"name":"Lingyao Sun","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyao","middleName":"","lastName":"Sun","suffix":""},{"id":549216802,"identity":"128d2a9f-9b73-4d8c-9982-4d1b576ef5c8","order_by":1,"name":"Dachuang Zhou","email":"","orcid":"","institution":"China Pharmaceutical 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Adjustment options include study-level and multilevel meta-regression, matching-adjusted indirect comparison, simulated treatment comparison, and network meta-interpolation. Comparative performance under graded violations of shared effect modification and practical guidance that links method choice to testable assumptions remain limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe built a four-trial anchored network and defined Trial 3 as the target population. Binary outcomes were generated on the probit scale with two covariates. We examined three scenarios for shared effect modification, namely shared, stronger interactions for treatment C by one half, and opposite directions. Each scenario had fifty replications. All methods estimated B versus C in the Trial 3 population on the probit scale. Performance metrics were bias, root mean squared error, and empirical coverage of the nominal ninety-five percent interval.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eML-NMR showed the smallest bias and coverage closest to the nominal level across scenarios. Study-level NMR maintained reasonable precision when shared effect modification held but showed wider intervals and higher bias when interactions differed. MAIC and STC were nearly unbiased when shared effect modification was valid and key modifiers were correctly identified, but both developed bias and undercoverage when interaction strength or direction diverged. NMI was comparatively stable, with reliability dependent on covariance stability. Conventional NMA performed worst under pronounced covariate mismatch.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAnalysts should first test prerequisite assumptions, including shared effect modification and homoscedasticity when relevant. 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