{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T00:02:56Z","timestamp":1774137776885,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["R03-DK138490"],"award-info":[{"award-number":["R03-DK138490"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"crossref","award":["R01-LM014085"],"award-info":[{"award-number":["R01-LM014085"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000066","name":"National Institute of Environmental Health Sciences","doi-asserted-by":"crossref","award":["R01-ES032808"],"award-info":[{"award-number":["R01-ES032808"]}],"id":[{"id":"10.13039\/100000066","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Large-scale prospective cohort studies collect longitudinal biospecimens alongside time-to-event outcomes to investigate biomarker dynamics in relation to disease risk. The nested case\u2013control (NCC) design provides a cost-effective alternative to full cohort biomarker studies while preserving statistical efficiency. Despite advances in joint modeling for longitudinal and time-to-event outcomes, few approaches address the unique challenges posed by NCC sampling, non-normally distributed biomarkers, and competing survival outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Motivated by the TEDDY study, we propose \u201cJM-NCC\u201d, a joint modeling framework designed for NCC studies with competing events. It integrates a generalized linear mixed-effects model for potentially non-normally distributed biomarkers with a cause-specific hazard model for competing risks. Two estimation methods are developed. fJM-NCC leverages NCC sub-cohort longitudinal biomarker data and full cohort survival and clinical metadata, while wJM-NCC uses only NCC sub-cohort data. Both simulation studies and an application to TEDDY microbiome dataset demonstrate the robustness and efficiency of the proposed methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Software is available at https:\/\/github.com\/Zhaoyn-oss\/JMNCC and archived on Zenodo at https:\/\/zenodo.org\/records\/18199759 (DOI: 10.5281\/zenodo.18199759).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag038","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T12:41:04Z","timestamp":1768999264000},"source":"Crossref","is-referenced-by-count":0,"title":["Joint modeling of longitudinal biomarker and survival outcomes with the presence of competing risk in the nested case\u2013control studies with application to the TEDDY microbiome 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