Estimation of the graft failure by current value joint model, and extension to alternative parameterization structures: Cohort study

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dc.authoridBakir Kayi, Alev/0000-0003-0664-5822
dc.contributor.authorBakir, Alev
dc.contributor.authorAtli, Zeynep
dc.contributor.authorKaya, Eda
dc.contributor.authorPekmezci, Salih
dc.contributor.authorSeyahi, Nurhan
dc.date.accessioned2025-03-23T19:34:17Z
dc.date.available2025-03-23T19:34:17Z
dc.date.issued2024
dc.departmentSinop Üniversitesi
dc.description.abstractIn clinical practice, individuals are followed up to predict the outcome event of interest, and their longitudinal measurements are collected on a regular or irregular basis. We aimed to examine the classical approach, joint model (JM), and alternative parameterization structures using data on the effect of time-varying longitudinal measurements on survival. The motivating cohort dataset included 158 consecutive kidney transplant recipients who had baseline and follow-up data. Although the longitudinal log-transformed estimated glomerular filtration rate (log[eGFR]) measurements and graft failure have an association clinically, the 2 processes are analyzed separately in the classical approach. In addition to the extended Cox model, the current value JM, the weighted cumulative effect JM, and dynamic predictions were performed in the study, by taking advantage of R codes. Of the 158 patients, 34.8% were males. The mean age was 29.8 +/- 10.9 years, and the median age was 26 years at the time of transplantation. The hazard ratio for graft failure was 8.80 for a 1-unit decrease in log(eGFR) in the extended Cox model, 10.58 in the current value JM, and 3.65 in the weighted cumulative effect JM. The presence of coronary heart disease was also found to be associated with log(eGFR): 0.199 (P = .03) for the current value JM and 0.197 (P = .03) for the weighted cumulative effect JM. The current value JM was identified as a better model than the extended Cox model and the weighted cumulative effect JM based on parameter and standard error comparison and goodness of fit criteria. JMs should be preferred, as they facilitate better clinical decisions by accounting for the varying slopes and longitudinal variation of estimated glomerular filtration rate among patients. Suitable types of models should be practiced depending on baseline biomarker levels, their trends over time, the distribution of the biomarkers, and the number of longitudinal biomarkers.
dc.identifier.doi10.1097/MD.0000000000040181
dc.identifier.issn0025-7974
dc.identifier.issn1536-5964
dc.identifier.issue42
dc.identifier.pmid39432613
dc.identifier.scopus2-s2.0-85206962970
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1097/MD.0000000000040181
dc.identifier.urihttps://hdl.handle.net/11486/5643
dc.identifier.volume103
dc.identifier.wosWOS:001358481200053
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofMedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250323
dc.subjectdynamic prediction
dc.subjecteGFR
dc.subjectjoint model
dc.subjectkidney graft failure
dc.subjectweighted cumulative effects
dc.titleEstimation of the graft failure by current value joint model, and extension to alternative parameterization structures: Cohort study
dc.typeArticle

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