Assessing Imputation Techniques for Missing Data in Small and Multicollinear Datasets: Insights from Craniofacial Morphometry

Assessing Imputation Techniques for Missing Data in Small and Multicollinear Datasets: Insights from Craniofacial Morphometry | 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 Assessing Imputation Techniques for Missing Data in Small and Multicollinear Datasets: Insights from Craniofacial Morphometry Norli Anida Abdullah, Firdaus Hariri, Mohamad Norikmal Fazli Hisam, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6947829/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in BMC Medical Research Methodology → Version 1 posted 17 You are reading this latest preprint version Abstract Background Analyses of craniofacial morphology are crucial for various medical and research applications, including the study of craniofacial development, dysmorphologies, and planning surgical interventions. Missing data in midfacial measurements can occur due to patient movement during imaging and scanning errors from the machine that may lead to biased conclusion and reduced statistical power. Objective This study evaluates various imputation techniques to determine the most effective approach for replacing missing values in a small, highly correlated, and high-dimensional midfacial morphometric dataset. Methods 42 midface variables were measured from 32 observations. The missing data structure was set to be at random with 268 (20%) missing values. Five common imputation techniques namely Mean/Median imputation, k-Nearest Neighbors (kNN), Multiple Imputation by Chained Equations (MICE), Random Forest (RF), and Decision Tree, were considered. The performance of the imputation technique was quantified using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Variance Preservation. Results RF Imputation demonstrated the best overall performance, with the lowest RMSE (1.3987) and MAE (0.4902), indicating a high level of accuracy in imputing missing values. It also maintained a relatively close to 1 variance preservation (0.8961), suggesting its effectiveness in retaining the original variability in the dataset. MICE present lower accuracy with high RMSE (3.0869) and MAE (1.1246) however appear to have the closest variance preservation to 1 (1.0580). Conclusion The findings emphasize the importance of selecting appropriate imputation techniques for small, high-dimensional, and correlated datasets such as those used in midfacial morphometry analysis. RF can provide a balance between accuracy and variance retention, while MICE may be preferable for preserving data distribution. Missing data craniofacial morphometry midface analysis data imputation kNN MICE EM variance preservation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviews received at journal 12 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 23 Jul, 2025 Editor invited by journal 01 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 01 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. 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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-6947829","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494262683,"identity":"a71c5afb-a73f-4a17-90f9-4a1d680a611c","order_by":0,"name":"Norli Anida Abdullah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYHACxgMJDDZglgTReoBa0kjVwsBwmAQt5uy9Dw48qDifuLaB+eBtHoZtiQ2EtFj2HDc4kHDmduK2A2zJ1jwMtwlrMbiRxnAgsQ2khcdMmgQt/84BtfB/I0VLwwGQLWxEajlzDBjIx5KNtx1mM7acY3DbmLCW422MD3/U2MluO9788MabituyBLUgADPYBAZHErRAgT3JOkbBKBgFo2DYAwDbrUPxf5QJTgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Malaya","correspondingAuthor":true,"prefix":"","firstName":"Norli","middleName":"Anida","lastName":"Abdullah","suffix":""},{"id":494262684,"identity":"7c242ead-bd9a-493b-9acf-15a5d4a8ed31","order_by":1,"name":"Firdaus Hariri","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Firdaus","middleName":"","lastName":"Hariri","suffix":""},{"id":494262685,"identity":"9c1a000f-a591-47d0-a717-ca09e36cbf0e","order_by":2,"name":"Mohamad Norikmal Fazli Hisam","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"Norikmal Fazli","lastName":"Hisam","suffix":""},{"id":494262686,"identity":"d60925ab-f43d-4025-b90e-a8ea971eb6dd","order_by":3,"name":"Siti Fatimah Binti Hassan","email":"","orcid":"","institution":"University of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Fatimah Binti","lastName":"Hassan","suffix":""}],"badges":[],"createdAt":"2025-06-22 05:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6947829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6947829/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12874-025-02762-4","type":"published","date":"2026-02-04T15:59:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102234100,"identity":"b5ec0ccf-05cf-4791-84ca-f8b47f1fd890","added_by":"auto","created_at":"2026-02-09 16:06:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":567421,"visible":true,"origin":"","legend":"","description":"","filename":"Seconddraft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6947829/v1_covered_10fc23ce-67bb-4ab4-8b74-a2719a7df205.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Imputation Techniques for Missing Data in Small and Multicollinear Datasets: Insights from Craniofacial Morphometry","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Missing data, craniofacial morphometry, midface analysis, data imputation, kNN, MICE, EM, variance preservation","lastPublishedDoi":"10.21203/rs.3.rs-6947829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6947829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAnalyses of craniofacial morphology are crucial for various medical and research applications, including the study of craniofacial development, dysmorphologies, and planning surgical interventions. Missing data in midfacial measurements can occur due to patient movement during imaging and scanning errors from the machine that may lead to biased conclusion and reduced statistical power.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study evaluates various imputation techniques to determine the most effective approach for replacing missing values in a small, highly correlated, and high-dimensional midfacial morphometric dataset.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e42 midface variables were measured from 32 observations. The missing data structure was set to be at random with 268 (20%) missing values. Five common imputation techniques namely Mean/Median imputation, k-Nearest Neighbors (kNN), Multiple Imputation by Chained Equations (MICE), Random Forest (RF), and Decision Tree, were considered. 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RF can provide a balance between accuracy and variance retention, while MICE may be preferable for preserving data distribution.\u003c/p\u003e","manuscriptTitle":"Assessing Imputation Techniques for Missing Data in Small and Multicollinear Datasets: Insights from Craniofacial Morphometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 08:44:24","doi":"10.21203/rs.3.rs-6947829/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T06:51:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"316008408048551115131347653395063772247","date":"2025-08-18T18:33:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326889595716405907266487595593981212096","date":"2025-08-16T10:31:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T19:48:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126788508919919395441211403924208527526","date":"2025-08-14T14:07:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320891978506763674129252573402371692682","date":"2025-08-14T06:54:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T22:01:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109757973552979247194019131163486315851","date":"2025-08-12T12:07:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T10:17:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84246367910404660379473439303144354057","date":"2025-08-12T09:43:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T15:31:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282929683772374053719497841442944185285","date":"2025-07-30T14:28:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T09:34:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T15:01:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-01T17:43:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-01T14:24:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-07-01T14:21:22+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":"0e44b909-2736-4017-9a98-720fdc0c3a31","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:03:01+00:00","versionOfRecord":{"articleIdentity":"rs-6947829","link":"https://doi.org/10.1186/s12874-025-02762-4","journal":{"identity":"bmc-medical-research-methodology","isVorOnly":false,"title":"BMC Medical Research Methodology"},"publishedOn":"2026-02-04 15:59:30","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-08-04 08:44:24","video":"","vorDoi":"10.1186/s12874-025-02762-4","vorDoiUrl":"https://doi.org/10.1186/s12874-025-02762-4","workflowStages":[]},"version":"v1","identity":"rs-6947829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6947829","identity":"rs-6947829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}