{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:43:44Z","timestamp":1792593824972,"version":"4.5.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG064803"],"award-info":[{"award-number":["R01AG064803"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["P30AG072958"],"award-info":[{"award-number":["P30AG072958"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>High-dimensional longitudinal data are increasingly available in biomedical research, especially from omics platforms, but pose substantial challenges for joint modeling with survival outcomes. These challenges include modeling complex temporal dynamics, accommodating cross-feature dependencies, and maintaining computational feasibility. We propose a novel joint modeling framework that addresses these issues using supervised low-rank functional tensor decomposition to capture latent structure in multivariate longitudinal data and proportional hazards modeling for time-to-event outcomes. The longitudinal process is represented as a multivariate functional tensor, with a low-rank approximation that incorporates supervision from baseline covariates. Estimation is performed using a likelihood-based Monte Carlo Expectation-Maximization algorithm, enabling coherent inference and individualized prediction. Our method produces dynamic predictions of both longitudinal feature trajectories and survival probabilities. Simulation studies demonstrate substantial improvements in estimation accuracy and predictive performance over a standard two-stage approach, particularly under high censoring and limited sample sizes. In application to the Alzheimer\u2019s Disease Neuroimaging Initiative lipidomics data, the proposed model explains over 99% of variation with four components, and identifies significant subject-level latent predictors of dementia onset. This framework provides a scalable and interpretable strategy for integrating high-dimensional longitudinal biomarkers into joint models for disease progression and risk stratification.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxag007","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T12:33:51Z","timestamp":1772973231000},"source":"Crossref","is-referenced-by-count":1,"title":["Joint modeling of high-dimensional longitudinal data and survival using supervised low-rank tensor decomposition"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0602-5861","authenticated-orcid":false,"given":"Mohammad Samsul","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Duke University , 2424 Erwin Road, Suite 1102 , Durham, NC 27705,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1858-5732","authenticated-orcid":false,"given":"Rima","family":"Kaddurah-Daouk","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Duke University , 2400 Pratt St, Box 102506 , Durham, NC 27710,","place":["United States"]},{"name":"Duke Institute of Brain Sciences, Duke University , 2400 Pratt St, Box 102506 , Durham, NC 27710,","place":["United States"]},{"name":"Department of Medicine, Duke University , 2400 Pratt St, Box 102506 , Durham, NC 27710,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4214-5809","authenticated-orcid":false,"given":"Sheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Duke University , 2424 Erwin Road, Suite 1102 , Durham, NC 27705,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"2026102110221659200_kxag007-B1","author":"Alam","year":"2024"},{"key":"2026102110221659200_kxag007-B2","first-page":"3708","article-title":"Alzheimer\u2019s disease facts and figures","volume":"20","author":"Alzheimer\u2019s Association","year":"2024","journal-title":"Alzheimer\u2019s Dementia"},{"key":"2026102110221659200_kxag007-B3","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1007\/s11357-023-00989-x","article-title":"Cerebrospinal fluid neutral lipids predict progression from mild cognitive impairment to alzheimer\u2019s disease","volume":"46","author":"Dakterzada","year":"2024","journal-title":"Geroscience"},{"key":"2026102110221659200_kxag007-B4","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.dadm.2017.07.006","article-title":"Blood-based metabolic signatures in alzheimer\u2019s disease. Alzheimer\u2019s & dementia: diagnosis","volume":"8","author":"de Leeuw","year":"2017","journal-title":"Alzheimers Dement (Amst)"},{"key":"2026102110221659200_kxag007-B5","doi-asserted-by":"crossref","first-page":"592302","DOI":"10.3389\/fneur.2020.592302","article-title":"Early stages of Alzheimer\u2019s disease: evolving the care team for optimal patient management","volume":"11","author":"Galvin","year":"2020","journal-title":"Front Neurol"},{"key":"2026102110221659200_kxag007-B6","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1177\/09622802231224631","article-title":"Dynamic prediction of survival using multivariate functional principal component analysis: a strict landmarking approach","volume":"33","author":"Gomon","year":"2024","journal-title":"Stat Methods Med Res"},{"key":"2026102110221659200_kxag007-B7","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1080\/10618600.2023.2257783","article-title":"Smooth and probabilistic parafac model with auxiliary covariates","volume":"33","author":"Guan","year":"2024","journal-title":"J Comput Graph Stat"},{"key":"2026102110221659200_kxag007-B8","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1080\/01621459.2022.2153689","article-title":"Guaranteed functional tensor singular value decomposition","volume":"119","author":"Han","year":"2024","journal-title":"J Am Stat Assoc"},{"key":"2026102110221659200_kxag007-B9","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1080\/01621459.2016.1273115","article-title":"Multivariate functional principal component analysis for data observed on different (dimensional) domains","volume":"113","author":"Happ","year":"2018","journal-title":"J Am Stat Assoc"},{"key":"2026102110221659200_kxag007-B10","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.chembiol.2018.10.008","article-title":"High-throughput plasma lipidomics: detailed mapping of the associations with cardiometabolic risk factors","volume":"26","author":"Huynh","year":"2019","journal-title":"Cell Chem Biol"},{"key":"2026102110221659200_kxag007-B11","doi-asserted-by":"crossref","first-page":"445","DOI":"10.31887\/DCNS.2013.15.4\/hjahn","article-title":"Memory loss in Alzheimer\u2019s disease","volume":"15","author":"Jahn","year":"2013","journal-title":"Dialogues Clin Neurosci"},{"key":"2026102110221659200_kxag007-B12","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1111\/biom.13427","article-title":"Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer\u2019s disease","volume":"78","author":"Li","year":"2022","journal-title":"Biometrics"},{"key":"2026102110221659200_kxag007-B13","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jmva.2015.02.016","article-title":"Supervised singular value decomposition and its asymptotic properties","volume":"146","author":"\u0398Li","year":"2016","journal-title":"J Multivar Anal"},{"key":"2026102110221659200_kxag007-B14","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1038\/s41398-021-01362-2","article-title":"Plasma lipidome is dysregulated in Alzheimer\u2019s disease and is associated with disease risk genes","volume":"11","author":"Liu","year":"2021","journal-title":"Transl Psychiatry"},{"key":"2026102110221659200_kxag007-B15","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1214\/18-EJS1421","article-title":"Supervised multiway factorization","volume":"12","author":"Lock","year":"2018","journal-title":"Electron J Stat"},{"key":"2026102110221659200_kxag007-B16","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1002\/alz.12661","article-title":"Biomarker clustering in autosomal dominant Alzheimer\u2019s disease","volume":"19","author":"Luckett","year":"2023","journal-title":"Alzheimer\u2019s Dementia"},{"key":"2026102110221659200_kxag007-B17","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/nm.3466","article-title":"Plasma phospholipids identify antecedent memory impairment in older adults","volume":"20","author":"Mapstone","year":"2014","journal-title":"Nat Med"},{"key":"2026102110221659200_kxag007-B18","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s11222-017-9758-2","article-title":"Supervised functional principal component analysis","volume":"28","author":"Nie","year":"2018","journal-title":"Stat Comput"},{"key":"2026102110221659200_kxag007-B19","doi-asserted-by":"crossref","first-page":"e494","DOI":"10.1038\/tp.2014.127","article-title":"Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer\u2019s disease","volume":"5","author":"Proitsi","year":"2015","journal-title":"Transl Psychiatry"},{"key":"2026102110221659200_kxag007-B20","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1038\/nn0403-345","article-title":"Alzheimer\u2019s disease: the cholesterol connection","volume":"6","author":"Puglielli","year":"2003","journal-title":"Nat Neurosci"},{"key":"2026102110221659200_kxag007-B21","doi-asserted-by":"crossref","DOI":"10.1007\/b98886","volume-title":"Applied functional data analysis: methods and case studies","author":"Ramsay","year":"2002"},{"key":"2026102110221659200_kxag007-B22","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1186\/s13195-023-01241-6","article-title":"Plasma and cerebrospinal fluid cholesterol esterification is hampered in Alzheimer\u2019s disease","volume":"15","author":"Turri","year":"2023","journal-title":"Alzheimer\u2019s Res Ther"},{"key":"2026102110221659200_kxag007-B23","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.jalz.2018.01.003","article-title":"Association of branched-chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer\u2019s disease: a prospective study in eight cohorts","volume":"14","author":"Tynkkynen","year":"2018","journal-title":"Alzheimer\u2019s Dement"},{"key":"2026102110221659200_kxag007-B24","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1214\/17-AOAS1059","article-title":"Dynamic prediction for multiple repeated measures and event time data: an application to Parkinson\u2019s disease","volume":"11","author":"Wang","year":"2017","journal-title":"Ann Appl Stat"},{"key":"2026102110221659200_kxag007-B25","doi-asserted-by":"crossref","first-page":"F1000","DOI":"10.12688\/f1000research.14506.1","article-title":"Current understanding of Alzheimer\u2019s disease diagnosis and treatment","volume":"7","author":"Weller","year":"2018","journal-title":"F1000Res"},{"key":"2026102110221659200_kxag007-B26","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1111\/jnc.12637","article-title":"Cholesterol as a causative factor in Alzheimer\u2019s disease: a debatable hypothesis","volume":"129","author":"Wood","year":"2014","journal-title":"J Neurochem"},{"key":"2026102110221659200_kxag007-B27","doi-asserted-by":"crossref","first-page":"330","DOI":"10.2307\/2533118","article-title":"A joint model for survival and longitudinal data measured with error","volume":"53","author":"Wulfsohn","year":"1997","journal-title":"Biometrics"},{"key":"2026102110221659200_kxag007-B28","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1111\/j.1541-0420.2011.01671.x","article-title":"Evaluating prognostic accuracy of biomarkers under competing risk","volume":"68","author":"Zheng","year":"2012","journal-title":"Biometrics"},{"key":"2026102110221659200_kxag007-B29","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1214\/23-AOAS1733","article-title":"Bayesian inference and dynamic prediction for multivariate longitudinal and survival data","volume":"17","author":"Zou","year":"2023","journal-title":"Ann Appl Stat"},{"key":"2026102110221659200_kxag007-B30","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1214\/24-AOAS1970","article-title":"Dynamic prediction with multivariate longitudinal outcomes and longitudinal magnetic resonance imaging data","volume":"19","author":"Zou","year":"2025","journal-title":"Ann Appl Stat"}],"container-title":["Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biostatistics\/article-pdf\/27\/1\/kxag007\/68021272\/kxag007.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biostatistics\/article-pdf\/27\/1\/kxag007\/68021272\/kxag007.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:22:26Z","timestamp":1792592546000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biostatistics\/article\/doi\/10.1093\/biostatistics\/kxag007\/8650781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,20]]}},"URL":"https:\/\/doi.org\/10.1093\/biostatistics\/kxag007","relation":{},"ISSN":["1465-4644","1468-4357"],"issn-type":[{"value":"1465-4644","type":"print"},{"value":"1468-4357","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,1]]},"published":{"date-parts":[[2026,1]]},"article-number":"kxag007"}}