{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T21:23:01Z","timestamp":1770153781651,"version":"3.49.0"},"reference-count":55,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>This study investigates mortality risk prediction in peritoneal dialysis (PD) patients through longitudinal biomarker analysis, comparing traditional and advanced statistical approaches. A retrospective cohort of 417 PD patients followed up between 1995 and 2016 at Erciyes University was analyzed, with serum albumin, creatinine, calcium, blood urea nitrogen (BUN), and phosphorus assessed as predictors of all-cause mortality. Statistical methods included Cox proportional hazards models, time-dependent covariates, and joint modeling (univariate and multivariate) for longitudinal-survival data integration. Joint models outperformed baseline, averaged, and time-dependent methods, with multivariate joint modeling yielding the highest predictive accuracy by incorporating inter-biomarker relationships. Serum albumin emerged as the most consistent mortality predictor, while creatinine and phosphorus showed significance in specific contexts. Other biomarkers, such as calcium and BUN, were less predictive. Dynamic prediction capabilities of joint models demonstrated enhanced alignment with patient outcomes, underscoring their utility in personalized medicine. This study highlights the importance of integrating temporal changes and biomarker interdependencies into survival analysis to improve risk stratification and clinical decision-making in PD patients. Future research should explore the broader applicability of these methods across diverse chronic disease populations.<\/jats:p>","DOI":"10.1371\/journal.pone.0320385","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T17:50:11Z","timestamp":1753725011000},"page":"e0320385","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling"],"prefix":"10.1371","volume":"20","author":[{"given":"Merve Basol","family":"Goksuluk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2752-7668","authenticated-orcid":true,"given":"Dincer","family":"Goksuluk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murat 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