Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients

Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients | 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 Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients Guanqing Chen, A James O'Malley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4014583/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Applied Network Science → Version 1 posted 7 You are reading this latest preprint version Abstract When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables. Bayesian inference Direct and indirect peer effects Diffusion of Robotic surgery Hierarchical network autocorrelation model Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementarydocumentANS.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2024 Read the published version in Applied Network Science → Version 1 posted Editorial decision: Revision requested 03 Apr, 2024 Reviews received at journal 20 Mar, 2024 Reviewers agreed at journal 06 Mar, 2024 Reviewers invited by journal 06 Mar, 2024 Editor assigned by journal 06 Mar, 2024 Submission checks completed at journal 05 Mar, 2024 First submitted to journal 04 Mar, 2024 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-4014583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276773861,"identity":"9df258fd-3389-4823-b0ea-60df966f41b3","order_by":0,"name":"Guanqing Chen","email":"","orcid":"","institution":"Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Guanqing","middleName":"","lastName":"Chen","suffix":""},{"id":276773862,"identity":"13ede3c5-54f9-4596-be9c-eba79480d943","order_by":1,"name":"A James O'Malley","email":"data:image/png;base64,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","orcid":"","institution":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth","correspondingAuthor":true,"prefix":"","firstName":"A","middleName":"James","lastName":"O'Malley","suffix":""}],"badges":[],"createdAt":"2024-03-04 23:52:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4014583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4014583/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41109-024-00627-1","type":"published","date":"2024-06-14T15:15:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58822863,"identity":"53dd4a38-f602-4819-b8e7-8d64325d8012","added_by":"auto","created_at":"2024-06-21 16:48:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":570558,"visible":true,"origin":"","legend":"","description":"","filename":"ANSComplexNetworkSpecialIssue.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4014583/v1_covered_fdb7e140-1647-4244-8ac1-1ae0d5e27766.pdf"},{"id":52128737,"identity":"ac6fde8f-f306-4df8-941a-5441a9e77e4d","added_by":"auto","created_at":"2024-03-07 07:24:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":344761,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydocumentANS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4014583/v1/7dd5f7b0efe9353fc267fd49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"applied-network-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apns","sideBox":"Learn more about [Applied Network Science](http://appliednetsci.springeropen.com/)","snPcode":"41109","submissionUrl":"https://submission.nature.com/new-submission/41109/3","title":"Applied Network Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bayesian inference, Direct and indirect peer effects, Diffusion of Robotic surgery, Hierarchical network autocorrelation model","lastPublishedDoi":"10.21203/rs.3.rs-4014583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4014583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. 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