A New Data Augmentation Method for Bayesian Semiparametric Proportional Hazards Model Analyzing Arbitrarily Censored Data

A New Data Augmentation Method for Bayesian Semiparametric Proportional Hazards Model Analyzing Arbitrarily Censored Data | 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 New Data Augmentation Method for Bayesian Semiparametric Proportional Hazards Model Analyzing Arbitrarily Censored Data Xin Zhi, Xiaoyan Lin, Lianming Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3690717/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Arbitrarily censored data have a more general data structure than conventional right-censored data or general interval-censored data and thus are more challenging to analyze. In this article, a novel Bayesian approach is developed for analyzing arbitrarily censored data under the semiparametric proportional hazards (PH) model. The proposed method adopts M-splines and I-splines to model the baseline hazard and cumulative baseline hazard functions in the PH model, respectively. A new two-stage data augmentation involving exponential and multinomial latent variables is proposed, leading to a nice form of augmented likelihood. Based on this likelihood, an easy-to-implement Gibbs sampler is developed. Simulation studies show that the proposed method works well in estimating both regression parameters and survival functions. A numerical comparison of our method with existing Bayesian methods is also provided. Two real data sets on colorectal cancer and childhood mortality are analyzed for illustration. Exponential latent variable Gibbs sampler Multinomial latent variable Splines Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-3690717","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":255713706,"identity":"466d4547-3bca-48df-bb58-3822210eccf2","order_by":0,"name":"Xin Zhi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYFACHoYDH4AUGzuEy9hAjJaDM0BamEnRwswDoonWYnD+7MHDNhXb5PmYGZg//GCwkd1wgIAWyYZzCYdzztw2bGNmYJPsYUgzJqiFn7HH4HBu221GkBag2w4nEtTCxsxjcNjy3217oBbmzwwM/wlr4WcDamFsuJ0I1MIgzcBwgLAWyR4eg4M9x24ntzEztkn2GCQbzySkxeD8GeMPP2pu285vbz784UeFnWwfIS1IABQjBsQrHwWjYBSMglGABwAAC/Y+Whl3t1IAAAAASUVORK5CYII=","orcid":"","institution":"University of South Carolina","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhi","suffix":""},{"id":255713709,"identity":"35971b50-5447-4a2f-ad2e-8463f1957da7","order_by":1,"name":"Xiaoyan Lin","email":"","orcid":"","institution":"University of South Carolina","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Lin","suffix":""},{"id":255713711,"identity":"276790ad-5c44-4237-8e25-522ec5262aa7","order_by":2,"name":"Lianming Wang","email":"","orcid":"","institution":"University of South Carolina","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lianming","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-12-01 06:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3690717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3690717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56359036,"identity":"f4b2b872-f4b8-46c8-8d01-e3435c847cba","added_by":"auto","created_at":"2024-05-13 07:05:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588044,"visible":true,"origin":"","legend":"","description":"","filename":"XinZhiPaperStatisticsandComputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3690717/v1_covered_513838e2-793d-4d2b-a349-4cc3071496bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New Data Augmentation Method for Bayesian Semiparametric Proportional Hazards Model Analyzing Arbitrarily Censored Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Exponential latent variable, Gibbs sampler, Multinomial latent variable, Splines","lastPublishedDoi":"10.21203/rs.3.rs-3690717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3690717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Arbitrarily censored data have a more general data structure than conventional right-censored data or general interval-censored data and thus are more challenging to analyze. In this article, a novel Bayesian approach is developed for analyzing arbitrarily censored data under the semiparametric proportional hazards (PH) model. The proposed method adopts M-splines and I-splines to model the baseline hazard and cumulative baseline hazard functions in the PH model, respectively. A new two-stage data augmentation involving exponential and multinomial latent variables is proposed, leading to a nice form of augmented likelihood. Based on this likelihood, an easy-to-implement Gibbs sampler is developed. Simulation studies show that the proposed method works well in estimating both regression parameters and survival functions. A numerical comparison of our method with existing Bayesian methods is also provided. Two real data sets on colorectal cancer and childhood mortality are analyzed for illustration.","manuscriptTitle":"A New Data Augmentation Method for Bayesian Semiparametric Proportional Hazards Model Analyzing Arbitrarily Censored Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-05 02:27:05","doi":"10.21203/rs.3.rs-3690717/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c830a14f-8199-47bd-8dc2-e692e4148025","owner":[],"postedDate":"December 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-13T06:57:16+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-05 02:27:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3690717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3690717","identity":"rs-3690717","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}