Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction

Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction | 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 Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction Sahar Souri Pilangorgi, Soheila Khodakarim, Zahra Shayan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194555/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Longitudinal data with excess zeros and survival data with a cured fraction are common in medical research, often exhibiting interdependence that complicates analysis. Joint modeling of these data types can address this interdependence, potentially improving estimation accuracy over separate models. This study investigates the performance of joint models compared to separate models under varying conditions of zero-inflation, cure rates, and sample sizes. Methods We analyzed 500 datasets with sample sizes of 500 and 800 as part of a simulation study that included 32 scenarios. Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) distributions were used to generate longitudinal count data, with zero-inflation rates of 43–50% and 69–75%, respectively. A mixture cure model was used to model survival data, which included cure rates of 47–50% and 59–66%. Joint models, fitted using Bayesian techniques with JAGS in R, connected longitudinal and survival processes through shared random effects. By comparing the bias in parameter estimates between joint and separate models, the performance of the models was assessed. Results Joint models consistently outperformed separate models, showing lower bias across most parameters. ZINB-based joint models exhibited less bias than ZIP models in high zero-inflation settings due to better handling of overdispersion, while ZIP models performed better with lower zero-inflation (43–50%). Larger sample sizes (800) reduced bias compared to smaller samples (500). Lower cure rates generally improved estimation accuracy, especially in joint models. Conclusion Joint modeling of zero-inflated longitudinal count data and survival data with a cure fraction provides more accurate parameter estimates than separate models, particularly in complex scenarios with high zero-inflation and cure rates. These findings highlight the importance of joint modeling in medical research involving interdependent longitudinal and survival outcomes. zero-inflated longitudinal count data mixture cure joint model Full Text Additional Declarations No competing interests reported. Supplementary Files appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Editor invited by journal 28 Jul, 2025 Editor assigned by journal 26 Jul, 2025 Submission checks completed at journal 26 Jul, 2025 First submitted to journal 23 Jul, 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. 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-7194555","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495943771,"identity":"a779a139-2b23-499f-9a2a-8d84dac72b37","order_by":0,"name":"Sahar Souri Pilangorgi","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Souri","lastName":"Pilangorgi","suffix":""},{"id":495943772,"identity":"7cc02272-0615-4601-aa48-ca7c2c24acde","order_by":1,"name":"Soheila Khodakarim","email":"data:image/png;base64,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","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Soheila","middleName":"","lastName":"Khodakarim","suffix":""},{"id":495943773,"identity":"0b1c4b5c-3139-4d4f-adde-b6a1964b0946","order_by":2,"name":"Zahra Shayan","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Shayan","suffix":""}],"badges":[],"createdAt":"2025-07-23 09:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7194555/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7194555/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88428220,"identity":"16152c32-db61-4a2e-a214-35fefd10193d","added_by":"auto","created_at":"2025-08-06 10:10:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":611655,"visible":true,"origin":"","legend":"","description":"","filename":"Khodakarim.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194555/v1_covered_91624905-3f18-4dd4-a31d-03bd3e04daad.pdf"},{"id":88425450,"identity":"7ab1e6c5-d043-440a-b77f-013bd6583537","added_by":"auto","created_at":"2025-08-06 09:53:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":630557,"visible":true,"origin":"","legend":"","description":"","filename":"appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7194555/v1/591b8d8e79411691f95706bf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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},"keywords":"zero-inflated, longitudinal count data, mixture cure, joint model","lastPublishedDoi":"10.21203/rs.3.rs-7194555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLongitudinal data with excess zeros and survival data with a cured fraction are common in medical research, often exhibiting interdependence that complicates analysis. Joint modeling of these data types can address this interdependence, potentially improving estimation accuracy over separate models. This study investigates the performance of joint models compared to separate models under varying conditions of zero-inflation, cure rates, and sample sizes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed 500 datasets with sample sizes of 500 and 800 as part of a simulation study that included 32 scenarios. Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) distributions were used to generate longitudinal count data, with zero-inflation rates of 43\u0026ndash;50% and 69\u0026ndash;75%, respectively. A mixture cure model was used to model survival data, which included cure rates of 47\u0026ndash;50% and 59\u0026ndash;66%. Joint models, fitted using Bayesian techniques with JAGS in R, connected longitudinal and survival processes through shared random effects. By comparing the bias in parameter estimates between joint and separate models, the performance of the models was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eJoint models consistently outperformed separate models, showing lower bias across most parameters. ZINB-based joint models exhibited less bias than ZIP models in high zero-inflation settings due to better handling of overdispersion, while ZIP models performed better with lower zero-inflation (43\u0026ndash;50%). Larger sample sizes (800) reduced bias compared to smaller samples (500). Lower cure rates generally improved estimation accuracy, especially in joint models.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eJoint modeling of zero-inflated longitudinal count data and survival data with a cure fraction provides more accurate parameter estimates than separate models, particularly in complex scenarios with high zero-inflation and cure rates. These findings highlight the importance of joint modeling in medical research involving interdependent longitudinal and survival outcomes.\u003c/p\u003e","manuscriptTitle":"Joint Modeling of Zero-Inflated Longitudinal Count Data and Survival Outcomes with Cure Fraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 09:45:54","doi":"10.21203/rs.3.rs-7194555/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T09:11:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T14:03:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254407305022559063208947189886418486850","date":"2025-08-26T13:54:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T17:23:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287512884448369731160360542066031252954","date":"2025-08-05T10:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-03T21:34:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-28T20:26:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-26T11:40:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-26T11:39:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-07-23T09:11:13+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":"774b3699-0515-4808-a901-61c79e1ae1cf","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T15:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 09:45:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7194555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194555","identity":"rs-7194555","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}