{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:03:25Z","timestamp":1792785805986,"version":"4.5.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":187,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health","award":["R03DK135437"],"award-info":[{"award-number":["R03DK135437"]}],"id":[{"id":"https:\/\/ror.org\/00adh9b73","id-type":"ROR","asserted-by":"crossref"}]}],"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>This paper presents a joint model of multivariate longitudinal data and multistate data with application to modeling and predicting autoantibody development in The Environmental Determinants of Diabetes in the Young (TEDDY) study. The model quantifies the risks of state transitions based on observed time-varying and non-time-varying risk factors. Based on the estimated model, a dynamic prediction approach is suggested to predict future state occupation probabilities using historical data. The proposed method can handle uncertainties in the observed data, due to measurement errors in the observed longitudinal data and interval censoring or missing information in the observed multistate data. For evaluating the predictions by the proposed approach, some performance metrics and their estimation are discussed. The proposed method is evaluated by some simulation studies. It is discussed in detail how this method can be used in analyzing the TEDDY data by properly handling the missing information and predicting future disease status using the proposed dynamic prediction algorithm.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxag018","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:07:27Z","timestamp":1783400847000},"source":"Crossref","is-referenced-by-count":0,"title":["Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring"],"prefix":"10.1093","volume":"27","author":[{"given":"Lu","family":"You","sequence":"first","affiliation":[{"name":"Health Informatics Institute, University of South Florida , 3650 Spectrum Blvd , Tampa, FL 33612,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-4287","authenticated-orcid":false,"given":"Falastin","family":"Salami","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, Lund University , Jan Waldenstr\u00f6ms gata 35 , Malm\u00f6 21428,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carina","family":"T\u00f6rn","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, Lund University , Jan Waldenstr\u00f6ms gata 35 , Malm\u00f6 21428,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1735-0499","authenticated-orcid":false,"given":"\u00c5ke","family":"Lernmark","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences, Lund University , Jan Waldenstr\u00f6ms gata 35 , Malm\u00f6 21428,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6243-6772","authenticated-orcid":false,"given":"Kendra","family":"Vehik","sequence":"additional","affiliation":[{"name":"Health Informatics Institute, University of South Florida , 3650 Spectrum Blvd , Tampa, FL 33612,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Health Informatics Institute, University of South Florida , 3650 Spectrum Blvd , Tampa, FL 33612,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peihua","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida , 2004 Mowry Rd , Gainesville, FL 32610,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roy","family":"Tamura","sequence":"additional","affiliation":[{"name":"Health Informatics Institute, University of South Florida , 3650 Spectrum Blvd , Tampa, FL 33612,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"2026102110245257000_kxag018-B1","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1002\/bimj.202000382","article-title":"Time-dependent ROC curve estimation for interval-censored data","volume":"64","author":"Beyene","year":"2022","journal-title":"Biom J"},{"key":"2026102110245257000_kxag018-B2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1111\/biom.12232","article-title":"Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks","volume":"71","author":"Blanche","year":"2015","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B3","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1093\/biostatistics\/kxr003","article-title":"Joint model with latent state for longitudinal and multistate data","volume":"12","author":"Dantan","year":"2011","journal-title":"Biostatistics"},{"key":"2026102110245257000_kxag018-B4","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/BF01437406","article-title":"Oscillation matrices with spline smoothing","volume":"24","author":"Demmler","year":"1975","journal-title":"Numer Math"},{"key":"2026102110245257000_kxag018-B5","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1080\/00949655.2020.1736071","article-title":"Cumulative\/dynamic ROC curve estimation under interval censorship","volume":"90","author":"D\u00edaz-Coto","year":"2020","journal-title":"J Stat Comput Simul"},{"key":"2026102110245257000_kxag018-B6","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1137\/1114019","article-title":"Non-parametric estimation of a multivariate probability density","volume":"14","author":"Epanechnikov","year":"1969","journal-title":"Theory Probab Appl"},{"key":"2026102110245257000_kxag018-B7","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1038\/s41591-020-0930-4","article-title":"A combined risk score enhances prediction of type 1 diabetes among susceptible children","volume":"26","author":"Ferrat","year":"2020","journal-title":"Nat Med"},{"key":"2026102110245257000_kxag018-B8","doi-asserted-by":"publisher","first-page":"3933","DOI":"10.1002\/sim.6972","article-title":"Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer","volume":"35","author":"Ferrer","year":"2016","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B9","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1002\/bimj.200410170","article-title":"A semi-Markov model based on generalized Weibull distribution with an illustration for HIV disease","volume":"47","author":"Foucher","year":"2005","journal-title":"Biom J"},{"key":"2026102110245257000_kxag018-B10","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1007\/s00125-015-3672-y","article-title":"Islet autoantibody phenotypes and incidence in children at increased risk for type 1 diabetes","volume":"58","author":"Giannopoulou","year":"2015","journal-title":"Diabetologia"},{"key":"2026102110245257000_kxag018-B11","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2307\/2531633","article-title":"Some diagnostic methods for Cox regression models through hazard smoothing","volume":"46","author":"Gray","year":"1990","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B12","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1093\/biomet\/asad073","article-title":"Maximum likelihood estimation for semiparametric regression models with interval-censored multistate data","volume":"111","author":"Gu","year":"2024","journal-title":"Biometrika"},{"key":"2026102110245257000_kxag018-B13","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.cardfail.2022.02.010","article-title":"2022 ACC\/AHA\/HFSA guideline for the management of heart failure","volume":"28","author":"Heidenreich","year":"2022","journal-title":"J Card Fail"},{"key":"2026102110245257000_kxag018-B14","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1111\/j.1541-0420.2006.00570.x","article-title":"Joint modeling of survival and longitudinal data: likelihood approach revisited","volume":"62","author":"Hsieh","year":"2006","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B15","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.2337\/dc15-1419","article-title":"Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association","volume":"38","author":"Insel","year":"2015","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v038.i08","article-title":"Multi-state models for panel data: the MSM package for R","volume":"38","author":"Jackson","year":"2011","journal-title":"J Stat Soft"},{"key":"2026102110245257000_kxag018-B17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1111\/pedi.12812","article-title":"Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children","volume":"20","author":"Jacobsen","year":"2019","journal-title":"Pediatr Diabetes"},{"key":"2026102110245257000_kxag018-B18","doi-asserted-by":"publisher","first-page":"bvad160","DOI":"10.1210\/jendso\/bvad160","article-title":"Understanding islet autoantibodies in prediction of type 1 diabetes","volume":"8","author":"Jia","year":"2023","journal-title":"J Endocr Soc"},{"key":"2026102110245257000_kxag018-B19","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1002\/sim.5534","article-title":"A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring","volume":"32","author":"Kapetanakis","year":"2013","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B20","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.2337\/dc18-2282","article-title":"Predicting islet cell autoimmunity and type 1 diabetes: an 8-year TEDDY study progress report","volume":"42","author":"Krischer","year":"2019","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B21","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.2337\/dc21-2612","article-title":"Predictors of the initiation of islet autoimmunity and progression to multiple autoantibodies and clinical diabetes: the TEDDY study","volume":"45","author":"Krischer","year":"2022","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B22","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1038\/s41467-022-28909-1","article-title":"Progression of type 1 diabetes from latency to symptomatic disease is predicted by distinct autoimmune trajectories","volume":"13","author":"Kwon","year":"2022","journal-title":"Nat Commun"},{"key":"2026102110245257000_kxag018-B23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1111\/biom.12252","article-title":"A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data","volume":"71","author":"Lange","year":"2015","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B24","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1002\/sim.5635","article-title":"Joint modeling quality of life and survival using a terminal decline model in palliative care studies","volume":"32","author":"Li","year":"2013","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B25","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/0169-2607(95)01641-6","article-title":"MARKOV: a computer program for multi-state Markov models with covariables","volume":"47","author":"Marshall","year":"1995","journal-title":"Comput Methods Programs Biomed"},{"key":"2026102110245257000_kxag018-B26","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1007\/s00125-022-05843-x","article-title":"Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering","volume":"66","author":"Mistry","year":"2023","journal-title":"Diabetologia"},{"key":"2026102110245257000_kxag018-B27","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1111\/rssc.12186","article-title":"A multistate model for correlated interval-censored life history data in caries research","volume":"66","author":"Pak","year":"2017","journal-title":"J R Stat Soc Ser C Appl Stat"},{"key":"2026102110245257000_kxag018-B28","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1002\/sim.6731","article-title":"Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach","volume":"35","author":"Proust-Lima","year":"2016","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B29","doi-asserted-by":"publisher","first-page":"2992","DOI":"10.1177\/0962280214536537","article-title":"Longitudinal data subject to irregular observation: a review of methods with a focus on visit processes, assumptions, and study design","volume":"25","author":"Pullenayegum","year":"2016","journal-title":"Stat Methods Med Res"},{"key":"2026102110245257000_kxag018-B30","doi-asserted-by":"crossref","DOI":"10.1201\/9781003138150","volume-title":"Statistical methods for dynamic disease screening and spatio-temporal disease surveillance","author":"Qiu","year":"2024"},{"key":"2026102110245257000_kxag018-B31","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1080\/00401706.2013.822423","article-title":"Univariate dynamic screening system: an approach for identifying individuals with irregular longitudinal behavior","volume":"56","author":"Qiu","year":"2014","journal-title":"Technometrics"},{"key":"2026102110245257000_kxag018-B32","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1111\/rssc.12573","article-title":"Dynamic disease screening by joint modeling of survival and longitudinal data","volume":"71","author":"Qiu","year":"2022","journal-title":"J R Stat Soc (Ser C)"},{"key":"2026102110245257000_kxag018-B33","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/00401706.2019.1604434","article-title":"Process monitoring ROC curve for evaluating dynamic screening methods","volume":"62","author":"Qiu","year":"2020","journal-title":"Technometrics"},{"key":"2026102110245257000_kxag018-B34","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1111\/j.0006-341x.2001.00253.x","article-title":"Nonparametric mixed effects models for unequally sampled noisy curves","volume":"57","author":"Rice","year":"2001","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B35","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1002\/sim.4205","article-title":"A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event","volume":"30","author":"Rizopoulos","year":"2011","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B36","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1111\/j.1467-9868.2008.00704.x","article-title":"Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data","volume":"71","author":"Rizopoulos","year":"2009","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2026102110245257000_kxag018-B37","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1002\/sim.1203","article-title":"Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects","volume":"21","author":"Royston","year":"2002","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B38","doi-asserted-by":"crossref","DOI":"10.1093\/acprof:oso\/9780195152968.001.0001","volume-title":"Applied longitudinal data analysis: modeling change and event occurrence","author":"Singer","year":"2003"},{"key":"2026102110245257000_kxag018-B39","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.2337\/dc13-0425","article-title":"The prediction of type 1 diabetes by multiple autoantibody levels and their incorporation into an autoantibody risk score in relatives of type 1 diabetic patients","volume":"36","author":"Sosenko","year":"2013","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B40","doi-asserted-by":"publisher","first-page":"808","DOI":"10.2337\/dc14-2426","article-title":"Predictors of progression from the appearance of islet autoantibodies to early childhood diabetes: the environmental determinants of diabetes in the young (TEDDY)","volume":"38","author":"Steck","year":"2015","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B41","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1111\/j.1399-5448.2007.00269.x","article-title":"The environmental determinants of diabetes in the young (TEDDY) study: study design","volume":"8","author":"TEDDY Study Group","year":"2007","journal-title":"Pediatric Diabetes"},{"key":"2026102110245257000_kxag018-B42","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1111\/biom.12986","article-title":"Longitudinal and time-to-drop-out joint models can lead to seriously biased estimates when the drop-out mechanism is at random","volume":"75","author":"Thomadakis","year":"2019","journal-title":"Biometrics"},{"key":"2026102110245257000_kxag018-B43","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2026102110245257000_kxag018-B44","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1177\/0962280209105541","article-title":"Model diagnostics for multi-state models","volume":"19","author":"Titman","year":"2010","journal-title":"Stat Methods Med Res"},{"key":"2026102110245257000_kxag018-B45","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1080\/07474938.2014.975640","article-title":"Inference for shared-frailty survival models with left-truncated data","volume":"35","author":"Van den Berg","year":"2016","journal-title":"Econom Rev"},{"key":"2026102110245257000_kxag018-B46","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.2337\/dc19-2547","article-title":"Hierarchical order of distinct autoantibody spreading and progression to type 1 diabetes in the TEDDY study","volume":"43","author":"Vehik","year":"2020","journal-title":"Diabetes Care"},{"key":"2026102110245257000_kxag018-B47","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1093\/biostatistics\/kxaa011","article-title":"Assessing the accuracy of predictive models with interval-censored data","volume":"23","author":"Wu","year":"2022","journal-title":"Biostatistics"},{"key":"2026102110245257000_kxag018-B48","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1002\/sim.7149","article-title":"A joint modelling approach for multistate processes subject to resolution and under intermittent observations","volume":"36","author":"Yiu","year":"2017","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B49","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1080\/00401706.2019.1625813","article-title":"An effective method for online disease risk monitoring","volume":"62","author":"You","year":"2020","journal-title":"Technometrics"},{"key":"2026102110245257000_kxag018-B50","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.1002\/sim.10079","article-title":"A discrete approximation method for modeling interval-censored multistate data","volume":"43","author":"You","year":"2024","journal-title":"Stat Med"},{"key":"2026102110245257000_kxag018-B51","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.1214\/24-aoas1889","article-title":"Joint modeling of multistate and nonparametric multivariate longitudinal data","volume":"18","author":"You","year":"2024","journal-title":"Ann Appl Stat"},{"key":"2026102110245257000_kxag018-B52","doi-asserted-by":"publisher","first-page":"e73645","DOI":"10.1371\/journal.pone.0073645","article-title":"Cognitive declines precede and predict functional declines in aging and Alzheimer\u2019s disease","volume":"8","author":"Zahodne","year":"2013","journal-title":"PLoS One"},{"key":"2026102110245257000_kxag018-B53","doi-asserted-by":"publisher","first-page":"3195","DOI":"10.1002\/sim.8663","article-title":"A multistate joint model for interval-censored event-history data subject to within-unit clustering and informative missingness, with application to neurocysticercosis research","volume":"39","author":"Zhang","year":"2020","journal-title":"Stat Med"}],"container-title":["Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biostatistics\/article-pdf\/27\/1\/kxag018\/68704949\/kxag018.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biostatistics\/article-pdf\/27\/1\/kxag018\/68704949\/kxag018.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:25:01Z","timestamp":1792592701000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biostatistics\/article\/doi\/10.1093\/biostatistics\/kxag018\/8725837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,20]]}},"URL":"https:\/\/doi.org\/10.1093\/biostatistics\/kxag018","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":"kxag018"}}