{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:21:58Z","timestamp":1787505718673,"version":"build-2736575974"},"reference-count":43,"publisher":"Wiley","issue":"18-19","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["P30AR070254"],"award-info":[{"award-number":["P30AR070254"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","award":["K24AR080217"],"award-info":[{"award-number":["K24AR080217"]}],"id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100029250","name":"Donald B. and Dorothy L. 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Greene Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015629","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001845","name":"Scleroderma Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001845","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate and timely prediction of critical events from longitudinal electronic health record (EHR) data is essential for precision medicine, particularly in complex chronic diseases with heterogeneous and evolving patient trajectories. Despite rapid growth in the number of statistical and machine learning methods, prediction remains challenging, especially for binary outcomes. Many valid approaches either lack transparency, ignore temporal dependence, or are difficult to dynamically update with new data, limiting clinical applicability. Existing dynamic prediction methods are designed primarily for continuous outcomes and do not directly extend to binary settings, which are common in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We use a Bayesian generalized linear mixed model approach to develop a cross\u2010validated sequential prediction (CVSP) algorithm for estimating the conditional probability of a binary outcome given all available historical data. The prediction model integrates population\u2010level fixed effects, lagged outcomes to capture shorter\u2010term dependence, and patient\u2010specific random intercepts to account for longer\u2010term dependence due to unobserved heterogeneity. Predictions are cross\u2010validated and generated sequentially at each visit with Monte Carlo integration, without refitting the model as new data accrue. Variable selection via bootstrap LASSO is incorporated to improve model parsimony and prediction validity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The framework is motivated by and applied to systemic sclerosis (SSc) patients with longitudinal assessments of proximal muscle weakness. The proposed method demonstrates superior discriminative performance (cross\u2010validated AUC of 0.86) compared to standard regression and machine learning approaches, while also maintaining good calibration. The CVSP approach allows efficient individualized predictions that incorporate a patient's entire medical history. Variable selection reduces the number of predictors while preserving accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This Bayesian CVSP framework provides a generalizable approach for individualized, visit\u2010by\u2010visit prediction of binary outcomes from longitudinal EHR data. By dynamically updating risk estimates without repeated refitting, it supports real\u2010time clinical decision\u2010making and advances precision medicine for chronic disease management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/sim.70692","type":"journal-article","created":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T02:00:51Z","timestamp":1786154451000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Individualized Prediction of Binary Clinical Outcomes Using Bayesian Generalized Linear Mixed Models With Application in Scleroderma"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4960-2579","authenticated-orcid":false,"given":"Zixing","family":"Liu","sequence":"first","affiliation":[{"name":"Division of Rheumatology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrianne","family":"Woods","sequence":"additional","affiliation":[{"name":"Division of Rheumatology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura K.","family":"Hummers","sequence":"additional","affiliation":[{"name":"Division of Rheumatology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ami A.","family":"Shah","sequence":"additional","affiliation":[{"name":"Division of Rheumatology Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott L.","family":"Zeger","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Rheumatology Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Biostatistics Johns Hopkins Bloomberg School of Public Health  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.13978"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI126120"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/acem.12876"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911\u2010016\u20100277\u20104"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2016.11.001"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.14366\/usg.16045"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.2196\/41030"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2019.02.004"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198524847.001.0001"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010023\u201010561\u2010w"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010018\u201036745\u2010x"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.01.005"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010022\u201000651\u20104"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/57358"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/73.1.13"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10594284"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874\u2010021\u201001439\u2010y"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1086\/260544"},{"key":"e_1_2_9_20_1","first-page":"114","volume-title":"Structural Analysis of Discrete Data and Econometric Applications","author":"Heckman J. 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