Abstract
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.
Original language | English (US) |
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Pages (from-to) | 43-59 |
Number of pages | 17 |
Journal | Journal of Applied Statistics |
Volume | 50 |
Issue number | 1 |
DOIs | |
State | Published - 2023 |
Keywords
- Competing risks
- functional principal component analysis
- joint model
- kidney transplant
- latent variables
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty