Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint

Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint | 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 Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint Peng Liu, Yijian Huang, Kwun Chuen Gary Chan, Ying Qing Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2046274/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jun, 2023 Read the published version in Statistics in Biosciences → Version 1 posted You are reading this latest preprint version Abstract Recurrent event data are frequently encountered in many longitudinal studies where each individual may experience more than one event. [1] proposed a comparability constraint to estimate the time trend for the gap times, where the gap time pairs that satisfy the constraint have the same conditional distribution. However, the comparable paired gap times are also independent. Therefore, the comparable gap time pairs will be subject to a stronger constraint than needed for the estimation. Thus their procedure is subject to information loss. Under the accelerated failure time model, we propose a new comparability constraint that can overcome the drawback mentioned above. The gap time pairs being selected by the proposed comparability constraint will still have the same distribution, but they do not need to be independent of each other. We prove that the new estimator will still be unbiased. However, the variance will be smaller than [1]’s estimator. Thus our method is superior to [1]. Numerical studies also confirm the theoretical findings. We apply the proposed method to the HIV Prevention Trial Network 052 study. Accelerated failure time model Comparability Gap time Rank regression Recurrent event data Full Text declarations The authors declare no competing interest Cite Share Download PDF Status: Published Journal Publication published 03 Jun, 2023 Read the published version in Statistics in Biosciences → 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-2046274","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":136531617,"identity":"152dd8a0-5419-4013-a5c7-da735d5c6c98","order_by":0,"name":"Peng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFAC5oYDDBUJcK4BEVoYgVrOALWwkaKFgbGNFC3mDIyNh3nnpcmZyzcwfvjBcNiYoBbLBsaGw7zbcowt2xiYJXsYDpsR1GJwAKylInHDMQYGaQaGwzZEaplTUQ/UwvybBC0NOQkGxxjYQLYQdphlM2PDwTnH0gw3HEtss+wxSCfsfXP25sMf3tQkyxscPnz4xo8Ka8MGgg5jhjOBEURURBKjZhSMglEwCkY6AABkpTjybBb5MgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0492-0029","institution":"University of Kent","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Liu","suffix":""},{"id":136531618,"identity":"f08f7840-e2a6-4226-a361-c57ec20f27eb","order_by":1,"name":"Yijian Huang","email":"","orcid":"","institution":"Emory University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijian","middleName":"","lastName":"Huang","suffix":""},{"id":136531619,"identity":"9634d666-f568-4814-9569-dcfb1d36cb47","order_by":2,"name":"Kwun Chuen Gary Chan","email":"","orcid":"","institution":"University of Washington Department of Biostatistics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kwun","middleName":"Chuen Gary","lastName":"Chan","suffix":""},{"id":136531620,"identity":"8085eb9d-7c82-4265-bd8b-1d405d5b7cbd","order_by":3,"name":"Ying Qing Chen","email":"","orcid":"","institution":"Stanford University Stanford Prevention Research Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"Qing","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-09-08 17:16:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2046274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2046274/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12561-023-09376-8","type":"published","date":"2023-06-03T21:03:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"","formattedTitle":"Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-2046274/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"declarations","content":"\u003cp\u003eThe authors declare no competing interest \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"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":"Accelerated failure time model, Comparability, Gap time, Rank regression, Recurrent event data","lastPublishedDoi":"10.21203/rs.3.rs-2046274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2046274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecurrent event data are frequently encountered in many longitudinal studies where each individual may experience more than one event. [1] proposed a comparability constraint to estimate the time trend for the gap times, where the gap time pairs that satisfy the constraint have the same conditional distribution. However, the comparable paired gap times are also independent. Therefore, the comparable gap time pairs will be subject to a stronger constraint than needed for the estimation. Thus their procedure is subject to information loss. Under the accelerated failure time model, we propose a new comparability constraint that can overcome the drawback mentioned above. The gap time pairs being selected by the proposed comparability constraint will still have the same distribution, but they do not need to be independent of each other. We prove that the new estimator will still be unbiased. However, the variance will be smaller than [1]’s estimator. Thus our method is superior to [1]. Numerical studies also confirm the theoretical findings. We apply the proposed method to the HIV Prevention Trial Network 052 study.\u003c/p\u003e","manuscriptTitle":"Semiparametric Trend Analysis for Stratified Recurrent Gap Times under Weak Comparability Constraint","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-19 20:08:46","doi":"10.21203/rs.3.rs-2046274/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":"c1dcf4cf-df46-4957-bdc6-9c4077c95fd4","owner":[],"postedDate":"September 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:42:25+00:00","versionOfRecord":{"articleIdentity":"rs-2046274","link":"https://doi.org/10.1007/s12561-023-09376-8","journal":{"identity":"statistics-in-biosciences","isVorOnly":false,"title":"Statistics in Biosciences"},"publishedOn":"2023-06-03 21:03:11","publishedOnDateReadable":"June 3rd, 2023"},"versionCreatedAt":"2022-09-19 20:08:46","video":"","vorDoi":"10.1007/s12561-023-09376-8","vorDoiUrl":"https://doi.org/10.1007/s12561-023-09376-8","workflowStages":[]},"version":"v1","identity":"rs-2046274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2046274","identity":"rs-2046274","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}