Comparison of Missing Data Imputation Methods on Mixture Cure Survival Analysis Results in a Cardiovascular Disease Cohort

Comparison of Missing Data Imputation Methods on Mixture Cure Survival Analysis Results in a Cardiovascular Disease Cohort | 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 Article Comparison of Missing Data Imputation Methods on Mixture Cure Survival Analysis Results in a Cardiovascular Disease Cohort Shokh Mukhtar Ahmad, Nawzad Muhammed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2970892/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 The goal of this study was to investigate the effect of missing data in the covariate variable on the amount of lost information in the cure mixture survival analysis model. For this purpose, data from patients of a cardiovascular cohort in Sulaimani Hospital were used. In the case of missing completely at random, in six situations, include full data and 10% to 50% missing data, AIC and BIC values of the models were compared. The results showed that the behavior of these two indicators was the same in comparing the models. Also, the choice of the appropriate imputation method for cured mixture models depends on the rate of missing data. In 10% of missing, due to the low number of missing individuals, the simple method such as mean was the best method. But for higher percentages, hot deck and regression methods performed better. Also, due to the skewed distribution of patients' age data, in cases with a missing of more than 40%, the median has shown better performance. Biological sciences/Computational biology and bioinformatics Health sciences/Cardiology Missing data Imputation Survival analysis Cured mixture models AIC BIC Cardiovascular Disease Full Text Additional Declarations No competing interests reported. Supplementary Files apply.xlsx 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-2970892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":204162454,"identity":"e25d69a3-0078-4750-a2db-a9c050a73224","order_by":0,"name":"Shokh Mukhtar Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYDACCQZmZjAtASIrEqDCbGBESAuIOkOyFsY2JC24gPzs5sPGBQzbEmfO7j/4uXJempxu+9kHDB/KDjPwsSdg1WJw51hy8gyG24mzZQ4zS57dlmNsdibdgHHGucMMbDwPsGuRyDE+zAPUMk8imUGycVtF4rYDaQzMvG1ALRLYbZGfgdDC/LNxDlDL+WcMzH/xaGG4kWOcDNIyWyKZTbKxISdx2w2gLYx4tBjcSEs25jG4bTxzRrKZZcOxNGOzG88YDvacS+fB5Rf5GcmHpXkqbsvOuJH4+GZDTbKc2fk0xgc/yqzl5NtxOAxiFxr/ABDzMDDg04IDkKFlFIyCUTAKhiMAAAAPWv7vh+FYAAAAAElFTkSuQmCC","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shokh","middleName":"Mukhtar","lastName":"Ahmad","suffix":""},{"id":204162455,"identity":"3a7efa07-c8ce-463c-a33a-56547221fd36","order_by":1,"name":"Nawzad Muhammed","email":"","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nawzad","middleName":"","lastName":"Muhammed","suffix":""}],"badges":[],"createdAt":"2023-05-23 10:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2970892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2970892/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41618377,"identity":"617dd10f-4512-44cb-b4ff-eb9eb96def3e","added_by":"auto","created_at":"2023-08-16 07:52:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":513400,"visible":true,"origin":"","legend":"","description":"","filename":"ComparisonofMissingDataImputationMethodsonSurvivalAnalysisResultsinaCardiovascularDiseaseCohort1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2970892/v1_covered_89753b3b-0765-4030-99a0-eaf356e94b9d.pdf"},{"id":37612169,"identity":"add27f33-01ec-462a-ab05-5a492921b676","added_by":"auto","created_at":"2023-05-29 13:06:19","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":220220,"visible":true,"origin":"","legend":"","description":"","filename":"apply.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2970892/v1/21c21b876eab0f8945e6365c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Missing Data Imputation Methods on Mixture Cure Survival Analysis Results in a Cardiovascular Disease Cohort","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, Imputation, Survival analysis, Cured mixture models, AIC, BIC, Cardiovascular Disease","lastPublishedDoi":"10.21203/rs.3.rs-2970892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2970892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The goal of this study was to investigate the effect of missing data in the covariate variable on the amount of lost information in the cure mixture survival analysis model. 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