{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:06:02Z","timestamp":1764777962391,"version":"3.46.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis. Our results demonstrate that functional regression approaches that implicitly incorporate historic information through random effects, provide superior discriminative ability compared to approaches that do not take historic information into account or explicitly model historic information through autoregressive terms. Semi-parametric modeling approaches show a benefit in terms of dynamic discriminative ability compared to the clinical practice of using a fixed threshold on the raw measured value. Under high degrees of sparsity the functional regression approaches are less advantageous compared to varying-coefficient models or quantile regression. The class imbalance of the outcome affects the historic and non-historic approaches in equal measure, with lower event rates reducing performance. Finally, the functional regression and varying-coefficient model were applied to a real-world clinical dataset to demonstrate their performance and application.<\/jats:p>","DOI":"10.1177\/09622802251374288","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T13:35:31Z","timestamp":1756992931000},"page":"2202-2218","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A comparison of semi-parametric statistical modeling approaches to dynamic classification of irregularly and sparsely sampled curves"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6011-7783","authenticated-orcid":false,"given":"Ruben","family":"Deneer","sequence":"first","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Department of Clinical Chemistry, Catharina Hospital, Eindhoven, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-0983","authenticated-orcid":false,"given":"Zhuozhao","family":"Zhan","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9157-7224","authenticated-orcid":false,"given":"Edwin","family":"Van den Heuvel","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5331-9806","authenticated-orcid":false,"given":"Astrid GM","family":"van Boxtel","sequence":"additional","affiliation":[{"name":"Catharina Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-1027","authenticated-orcid":false,"given":"Arjen-Kars","family":"Boer","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry, Catharina Hospital, Eindhoven, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9375-4730","authenticated-orcid":false,"given":"Natal AW","family":"van Riel","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Department of Vascular Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1243-9251","authenticated-orcid":false,"given":"Volkher","family":"Scharnhorst","sequence":"additional","affiliation":[{"name":"Eindhoven University of Technology"},{"name":"Department of Clinical Chemistry, Catharina Hospital, Eindhoven, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/2533118"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-030718-105048"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-985X.00258"},{"key":"e_1_3_3_5_2","first-page":"523","article-title":"Prediction of pregnancy: a joint model for longitudinal and binary data","volume":"4","author":"Horrocks J","year":"2009","unstructured":"Horrocks J, van Den Heuvel MJ. Prediction of pregnancy: a joint model for longitudinal and binary data. Bayes Anal 2009; 4: 523\u2013538.","journal-title":"Bayes Anal"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201900044"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssa.12611"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201600238"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/0962280218811837"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12562"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjcard.2008.04.069"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19990530)18:10<1249::AID-SIM125>3.0.CO;2-#"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0258(20000815)19:15<1969::AID-SIM515>3.0.CO;2-Y"},{"key":"e_1_3_3_14_2","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/bimj.200310020","article-title":"A mixed model approach to discriminant analysis with longitudinal data","volume":"46","author":"Wernecke KD","year":"2004","unstructured":"Wernecke KD, Kalb G, Schink T, et al. A mixed model approach to discriminant analysis with longitudinal data. Biomet J: J Math Methods Biosci 2004; 46: 246\u2013254.","journal-title":"Biomet J: J Math Methods Biosci"},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1002\/bimj.200800157","article-title":"Classification of therapy resistance based on longitudinal biomarker profiles","volume":"51","author":"Kohlmann M","year":"2009","unstructured":"Kohlmann M, Held L, Grunert VP. Classification of therapy resistance based on longitudinal biomarker profiles. Biomet J: J Math Methods Biosci 2009; 51: 610\u2013626.","journal-title":"Biomet J: J Math Methods Biosci"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm041"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3849"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1177\/0962280216674496"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00297"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/den382"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2000.00487.x"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4405"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/02664763.2013.859235"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1002\/sta4.98"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9469.2005.00429.x"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.tm08564"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201000142"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2015.08.020"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1993.tb01939.x"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781315370279"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.3109\/03014460.2012.694475"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2271"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000000623"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.4172\/2155-6180.1000354"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1987.10478440"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1038425655"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1725521"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-017-9744-8"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001745"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1081\/SAC-200047072"},{"key":"e_1_3_3_41_2","volume-title":"Mixed-effects models in S and S-PLUS","author":"Pinheiro J","year":"2006","unstructured":"Pinheiro J, Bates D. Mixed-effects models in S and S-PLUS. New York, NY: Springer Science & Business Media, 2006."},{"key":"e_1_3_3_42_2","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Vienna Austria 2022. https:\/\/www.R-project.org\/."},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.08.1038"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehx383"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2020.01.025"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12872-018-0893-2"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2018.00182"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201700013"},{"key":"e_1_3_3_49_2","volume-title":"Principal component analysis for special types of data","author":"Jolliffe IT","year":"2002","unstructured":"Jolliffe IT. Principal component analysis for special types of data. New York, NY: Springer, 2002."}],"container-title":["Statistical Methods in Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802251374288","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09622802251374288","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802251374288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:02:51Z","timestamp":1764777771000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09622802251374288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":48,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1177\/09622802251374288"],"URL":"https:\/\/doi.org\/10.1177\/09622802251374288","relation":{},"ISSN":["0962-2802","1477-0334"],"issn-type":[{"type":"print","value":"0962-2802"},{"type":"electronic","value":"1477-0334"}],"subject":[],"published":{"date-parts":[[2025,9,4]]}}}