{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:50:22Z","timestamp":1791381022270,"version":"4.3.3"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL159213"],"award-info":[{"award-number":["R01HL159213"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK108070"],"award-info":[{"award-number":["R01DK108070"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Modern biomedical studies increasingly collect hybrid data, consisting of functional observations (e.g., curves or images) and multiple scalar (vector\u2010valued) clinical variables. Integrating these modalities in an unsupervised learning framework can improve patient subgroup identification and interpretation, yet most existing clustering approaches handle only a single data type. We introduce a clustering framework for hybrid data combining multivariate functional and vector outcomes. The method first applies hybrid principal component analysis to capture joint mean structures and modes of variation across modalities, then iteratively reassigns subjects using a hybrid distance metric based on cluster\u2010specific Karhunen\u2013Lo\u00e9ve expansions. This approach accommodates multiple functions of varying dimensions, integrates contributions from functional and vector components in mean and covariance structures, and avoids parametric distributional assumptions. Simulation studies show that the proposed method outperforms competing approaches when subgroup information, in terms of mean and\/or variance differences, is either distributed across modalities or concentrated in a single modality. We illustrate the method using renal radionuclide imaging data, including baseline and diuretic renogram curves and pharmacokinetic variables from patients with suspected kidney obstruction. The method identifies clinically meaningful subgroups consistent with expert assessment, demonstrating its potential to support data\u2010driven decision\u2010making in multimodal biomedical\u00a0studies.<\/jats:p>","DOI":"10.1002\/bimj.70184","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:02:01Z","timestamp":1790247721000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Hybrid Functional and Vector Data"],"prefix":"10.1002","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3283-8145","authenticated-orcid":false,"given":"Sekha D.","family":"Daluwatumulle","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics Emory University Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0811-2296","authenticated-orcid":false,"given":"Jeong Hoon","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science University of Texas Medical Branch Galveston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3697-5155","authenticated-orcid":false,"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amita K.","family":"Manatunga","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics Emory University Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"e_1_2_9_2_1","volume-title":"An Introduction to Multivariate Statistical Analysis","author":"Anderson T. 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