Bivariate joint modelling for mixed continuous and binary responses in the presence of non-monotone, non-ignorable missingness: The case of prostate cancer

Bivariate joint modelling for mixed continuous and binary responses in the presence of non-monotone, non-ignorable missingness: The case of prostate cancer | 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 Short Report Bivariate joint modelling for mixed continuous and binary responses in the presence of non-monotone, non-ignorable missingness: The case of prostate cancer Madiha Liaqat, Shahid Kamal, Florian Fischer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3892077/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 Joint modelling for mixed longitudinal responses has played a prominent part in disease decision-making. It is based on a joint strategy of estimating joint likelihood with shared random effects. Non-ignorable missingness in outcomes increases complexity in joint model; a shared parameter model is proposed to incorporate non-ignorable missing data for joint modelling of longitudinal responses and missing data mechanism. Parameters are estimated under the Bayesian paradigm and implemented via Markov chain Monte Carlo (MCMC) methods with Gibbs sampler. To demonstrate the effectiveness of the proposed method, the joint model is applied to analyze a prostate cancer dataset. The objective is to assess whether there is an association between two mixed longitudinal biomarkers, which could have important implications for understanding disease progression and guiding treatment decisions. The dataset contains non-monotone missingness pattern. To evaluate the performance and robustness of the proposed joint model, simulation studies are conducted. missing data missingness oncology prostate 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-3892077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":269112337,"identity":"012ff6dd-1320-44ad-bea7-33985298c7e7","order_by":0,"name":"Madiha Liaqat","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Madiha","middleName":"","lastName":"Liaqat","suffix":""},{"id":269112338,"identity":"e1179461-6057-4425-80cd-2e5b8b41af03","order_by":1,"name":"Shahid Kamal","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Shahid","middleName":"","lastName":"Kamal","suffix":""},{"id":269112339,"identity":"eb6b43a4-5694-4ced-84ca-bea8f8fd899f","order_by":2,"name":"Florian Fischer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie2PsWrDMBCGrwis5YxXmxb3Fa4IDIVCXkXGq2kDXTIEIgg4W2c/jovAWdy9hVKSxXPAUFoIoZKbTnE8d9A36H5O+jgdgMPxD+Erc0gblgDMVAwATSE49k9BfbyywSpXkeoVGlf6UP0qd1RZBUYUxurdZv4OyPm2m84+UKxfnrvZdB8Dz6phxctKWbcmoLgsm0dMmvssaogEYDs4ZmJegvS0DR7zC4nJK1KkiFIV5jQ8xSoHbQJvmX+QKEoU30ZZqPBhd1ZJC6tAwnwlkUJM7BQJYX5mfS+D9EljvwvWEsMmT24ViZsC2+GP8aW++PrUMQbrbYdzOQlWjXhT+/g64NlmcMyfetryxt47HA6HY5QfsvhOLaVQ1DcAAAAASUVORK5CYII=","orcid":"","institution":"Charité – Universitätsmedizin Berlin","correspondingAuthor":true,"prefix":"","firstName":"Florian","middleName":"","lastName":"Fischer","suffix":""}],"badges":[],"createdAt":"2024-01-23 21:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3892077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3892077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55265664,"identity":"50936993-3959-43f2-a449-7732f8bc0f0c","added_by":"auto","created_at":"2024-04-25 02:12:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":343813,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3892077/v1_covered_cb35c62c-25a9-4dc3-8d69-a1b87cdf0db2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bivariate joint modelling for mixed continuous and binary responses in the presence of non-monotone, non-ignorable missingness: The case of prostate cancer","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":"missing data, missingness, oncology, prostate ","lastPublishedDoi":"10.21203/rs.3.rs-3892077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3892077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Joint modelling for mixed longitudinal responses has played a prominent part in disease decision-making. 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