Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis
| Autor: | Haolun Shi, Ying Zhang, Jiguo Cao, Liangliang Wang, Jianghu (James) Dong |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | J Appl Stat |
| ISSN: | 1360-0532 0266-4763 |
| Popis: | In many clinical studies, longitudinal biomarkers are often used to monitor the progression of a disease. For example, in a kidney transplant study, the glomerular filtration rate (GFR) is used as a longitudinal biomarker to monitor the progression of the kidney function and the patient's state of survival that is characterized by multiple time-to-event outcomes, such as kidney transplant failure and death. It is known that the joint modelling of longitudinal and survival data leads to a more accurate and comprehensive estimation of the covariates' effect. While most joint models use the longitudinal outcome as a covariate for predicting survival, very few models consider the further decomposition of the variation within the longitudinal trajectories and its effect on survival. We develop a joint model that uses functional principal component analysis (FPCA) to extract useful features from the longitudinal trajectories and adopt the competing risk model to handle multiple time-to-event outcomes. The longitudinal trajectories and the multiple time-to-event outcomes are linked via the shared functional features. The application of our model on a real kidney transplant data set reveals the significance of these functional features, and a simulation study is carried out to validate the accurateness of the estimation method. |
| Databáze: | OpenAIRE |
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