A Leave-One-Out Algorithm for Contribution Analysis in Additive Component Network Meta-Analysis

A Leave-One-Out Algorithm for Contribution Analysis in Additive Component Network Meta-Analysis | 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 A Leave-One-Out Algorithm for Contribution Analysis in Additive Component Network Meta-Analysis Yunhe Mao, Yiwen Shen, Qinbo Yang, Qingyang Shi, Sheyu Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6524959/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Aug, 2025 Read the published version in BMC Medical Research Methodology → Version 1 posted 9 You are reading this latest preprint version Abstract Background Component network meta-analysis (CNMA) enables disentangling individual treatment component effects but faces challenges in quantifying evidence contributions due to path enumeration complexity and component pathway unidentifiability. This study aims to develop a novel leave-one-out algorithm to address these limitations and enhance the interpretability of contribution metrics for individual component effects. Methods We propose a precision-centric leave-one-out algorithm grounded in jackknife principles. By iteratively removing direct comparisons and measuring inverse variance perturbations, the method quantifies statistical leverage via a contribution matrix. Parameter decomposition separates direct evidence from additive network evidence, ensuring component effects are estimated without contamination while independently weighting direct evidence. In the absence of prior benchmarks, we employed a surrogating internal validation through linear weighting to validate the congruence between contribution-weighted predictions and model-derived estimates. Results Application across illustrative scenarios demonstrated the algorithm’s utility in identifying critical comparisons that stabilize component estimates. Validation on a real dataset (21 components, 40 interventions, 66 comparisons) revealed high congruence between contribution-weighted predictions and additive model estimates: Pearson correlation r = 0.953 (p < 0.001), explained variance R² = 0.907, and mean absolute error MAE = 0.172. Coherence tests showed minimal discrepancies (< 0.1%) between combined estimates (integrating direct and additive evidence) and full model estimates. Conclusions The leave-one-out algorithm redefines contribution analysis in additive CNMA by replacing path enumeration with precision leverage quantification. It resolves unidentifiability challenges, enhances evidence transparency, and supports optimization of multi-component interventions. Limitations include computational scalability and heuristic redistribution rules, warranting future refinements. Component network meta-analysis contribution analysis leave-one-out method evidence synthesis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Aug, 2025 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 16 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 04 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers agreed at journal 01 May, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 26 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 24 Apr, 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. 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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-6524959","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449764004,"identity":"928a8029-4a82-4123-be7e-40a2aa8585ac","order_by":0,"name":"Yunhe Mao","email":"","orcid":"","institution":"Sports Medicine Center, West China Hospital, Sichuan University, Chengdu.","correspondingAuthor":false,"prefix":"","firstName":"Yunhe","middleName":"","lastName":"Mao","suffix":""},{"id":449764005,"identity":"69470fb2-4133-4736-b046-faad1b55577b","order_by":1,"name":"Yiwen Shen","email":"","orcid":"","institution":"Hong Kong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yiwen","middleName":"","lastName":"Shen","suffix":""},{"id":449764006,"identity":"c2c6d3c6-abb0-48b5-962d-7356dc811c22","order_by":2,"name":"Qinbo Yang","email":"","orcid":"","institution":"Department of Nephrology, West China Hospital, Sichuan University, Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Qinbo","middleName":"","lastName":"Yang","suffix":""},{"id":449764007,"identity":"4c7449b4-7457-4452-9aaf-166c376c31b9","order_by":3,"name":"Qingyang Shi","email":"","orcid":"","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Qingyang","middleName":"","lastName":"Shi","suffix":""},{"id":449764008,"identity":"3b62a93e-b6a3-4ac0-a54e-61bb5b7826d5","order_by":4,"name":"Sheyu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACPmYGNhAtByIOPCBGCxtUizFYSwJRWhggWhIbQCRxWtjZnz342HY4fX7Y4YdAW+zkdBsIOozH3HBm2+HcjbfTDIBako3NDhDWwibNC9IyOwGk5UDiNsJa2J9J/wU6zHB2+gditTCYSTO2HU6Ql84h2hYeM8mec+mGG6RzCg4kGBDhF37+488kfpRZy8vPTt/84UOFnRxBLWDACIwaA7BKA2KUg8EfBgb5BqJVj4JRMApGwUgDAMTzP9X2UYtTAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Endocrinology and Metabolism, MAGIC China Center, West China Hospital of Sichuan University, Chengdu","correspondingAuthor":true,"prefix":"","firstName":"Sheyu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-25 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Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Component network meta-analysis, contribution analysis, leave-one-out method, evidence synthesis","lastPublishedDoi":"10.21203/rs.3.rs-6524959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6524959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eComponent network meta-analysis (CNMA) enables disentangling individual treatment component effects but faces challenges in quantifying evidence contributions due to path enumeration complexity and component pathway unidentifiability. This study aims to develop a novel leave-one-out algorithm to address these limitations and enhance the interpretability of contribution metrics for individual component effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe propose a precision-centric leave-one-out algorithm grounded in jackknife principles. By iteratively removing direct comparisons and measuring inverse variance perturbations, the method quantifies statistical leverage via a contribution matrix. Parameter decomposition separates direct evidence from additive network evidence, ensuring component effects are estimated without contamination while independently weighting direct evidence. In the absence of prior benchmarks, we employed a surrogating internal validation through linear weighting to validate the congruence between contribution-weighted predictions and model-derived estimates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApplication across illustrative scenarios demonstrated the algorithm\u0026rsquo;s utility in identifying critical comparisons that stabilize component estimates. Validation on a real dataset (21 components, 40 interventions, 66 comparisons) revealed high congruence between contribution-weighted predictions and additive model estimates: Pearson correlation r\u0026thinsp;=\u0026thinsp;0.953 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explained variance \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.907, and mean absolute error MAE\u0026thinsp;=\u0026thinsp;0.172. Coherence tests showed minimal discrepancies (\u0026lt;\u0026thinsp;0.1%) between combined estimates (integrating direct and additive evidence) and full model estimates.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe leave-one-out algorithm redefines contribution analysis in additive CNMA by replacing path enumeration with precision leverage quantification. It resolves unidentifiability challenges, enhances evidence transparency, and supports optimization of multi-component interventions. Limitations include computational scalability and heuristic redistribution rules, warranting future refinements.\u003c/p\u003e","manuscriptTitle":"A Leave-One-Out Algorithm for Contribution Analysis in Additive Component Network Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:29:29","doi":"10.21203/rs.3.rs-6524959/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-16T08:13:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T19:09:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-04T18:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204766719541220368752255980007048387064","date":"2025-05-03T11:49:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184686811244948082730764228838920743035","date":"2025-05-01T16:05:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T05:07:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-26T08:37:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T08:34:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-04-25T03:45:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ab5dae4-c254-4447-9051-63dcbb959b24","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:00:06+00:00","versionOfRecord":{"articleIdentity":"rs-6524959","link":"https://doi.org/10.1186/s12874-025-02619-w","journal":{"identity":"bmc-medical-research-methodology","isVorOnly":false,"title":"BMC Medical Research Methodology"},"publishedOn":"2025-08-07 15:57:13","publishedOnDateReadable":"August 7th, 2025"},"versionCreatedAt":"2025-04-30 11:29:29","video":"","vorDoi":"10.1186/s12874-025-02619-w","vorDoiUrl":"https://doi.org/10.1186/s12874-025-02619-w","workflowStages":[]},"version":"v1","identity":"rs-6524959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6524959","identity":"rs-6524959","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}