Functional joint models for chronic kidney disease in kidney transplant recipients
| Autor: | Jiguo Cao, Jagbir Gill, Clifford D. Miles, Troy J. Plumb, Jianghu James Dong |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
medicine.medical_specialty Waiting Lists Epidemiology Kidney 01 natural sciences Kidney transplant 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Health Information Management medicine Humans 030212 general & internal medicine Renal Insufficiency Chronic 0101 mathematics Intensive care medicine Kidney transplantation urogenital system business.industry medicine.disease Kidney Transplantation medicine.anatomical_structure business Glomerular Filtration Rate Kidney disease |
| Zdroj: | Statistical Methods in Medical Research. 30:1932-1943 |
| ISSN: | 1477-0334 0962-2802 |
| Popis: | This functional joint model paper is motivated by a chronic kidney disease study post kidney transplantation. The available kidney organ is a scarce resource because millions of end-stage renal patients are on the waiting list for kidney transplantation. The life of the transplanted kidney can be extended if the progression of the chronic kidney disease stage can be slowed, and so a major research question is how to extend the transplanted kidney life to maximize the usage of the scarce organ resource. The glomerular filtration rate is the best test to monitor the progression of the kidney function, and it is a continuous longitudinal outcome with repeated measures. The patient’s survival status is characterized by time-to-event outcomes including kidney transplant failure, death with kidney function, and death without kidney function. Few studies have been carried out to simultaneously investigate these multiple clinical outcomes in chronic kidney disease stage patients based on a joint model. Therefore, this paper proposes a new functional joint model from this clinical chronic kidney disease study. The proposed joint models include a longitudinal sub-model with a flexible basis function for subject-level trajectories and a competing-risks sub-model for multiple time-to event outcomes. The different association structures can be accomplished through a time-dependent function of shared random effects from the longitudinal process or the whole longitudinal history in the competing-risks sub-model. The proposed joint model that utilizes basis function and competing-risks sub-model is an extension of the standard linear joint models. The application results from the proposed joint model can supply some useful clinical references for chronic kidney disease study post kidney transplantation. |
| Databáze: | OpenAIRE |
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