{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:26:41Z","timestamp":1787099201589,"version":"build-2736575974"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":49,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"publisher","award":["MC_UU_00040\/02"],"award-info":[{"award-number":["MC_UU_00040\/02"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"publisher","award":["MR\/V020595\/1"],"award-info":[{"award-number":["MR\/V020595\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011493","name":"MacMillan Center for International and Area Studies","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011493","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ulverscroft Vision Research Group at University College London"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12271047"],"award-info":[{"award-number":["12271047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>There is growing interest in the role of within-individual variability (WIV) in biomarker trajectories for assessing disease risk and progression. A trajectory-based definition that has attracted recent attention characterizes WIV as the curvature-based roughness of the latent biomarker trajectory, and we refer to the resulting measure as trajectory-based biomarker WIV (TB-WIV). To evaluate TB-WIV associations with clinical outcomes and perform dynamic risk prediction, joint models for longitudinal and time-to-event data (JM) are necessary. However, specifying the longitudinal trajectory is critical and poses methodological challenges. In this work, we investigate three Bayesian semiparametric approaches for longitudinal modeling and TB-WIV estimation. We propose two new approaches based on Bayesian penalized splines (P-splines) and functional principal component analysis, and adapt an existing semiparametric approach to the Bayesian JM setting. Through simulation studies, we evaluate parameter estimation, detection of TB-WIV associations, estimation of their raw and standardized magnitudes, and survival prediction, providing insights for method choice and highlighting the need for caution when interpreting raw TB-WIV association magnitudes. We apply the approaches to UK Cystic Fibrosis Registry data, where we identify significant positive associations between lung function TB-WIV and mortality risk in patients with cystic fibrosis, after adjustment for the underlying lung function level, and provide evidence that TB-WIV adds information for survival prediction.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag146","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T12:06:42Z","timestamp":1785586002000},"source":"Crossref","is-referenced-by-count":0,"title":["Bayesian semiparametric modeling of biomarker variability in joint models"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9409-9358","authenticated-orcid":false,"given":"Sida","family":"Chen","sequence":"first","affiliation":[{"name":"MRC Biostatistics Unit, University of Cambridge , Cambridge CB2 0SR ,","place":["United 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