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. 2021 Sep 22;50(1):43-59.
doi: 10.1080/02664763.2021.1981256. eCollection 2023.

Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis

Jianghu James Dong  1   2 , Haolun Shi  3 , Liangliang Wang  3 , Ying Zhang  1 , Jiguo Cao  3
Affiliations

Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis

Jianghu James Dong et al. J Appl Stat. .

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.

Keywords: Competing risks; functional principal component analysis; joint model; kidney transplant; latent variables.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
The 4 clusters of observable GFR curves by FPC scores from our preliminary analysis. The thick blue curve is the average of GFR curves in each panel.
Figure 2.
Figure 2.
The first four leading functional principal components that estimated from the observed longitudinal GFR data and the recovered GFR data by incorporating survival data information when jointly modelling the longitudinal and survival outcomes.
Figure A1.
Figure A1.
The first four leading functional principal components (FPCs) that estimated from functional principal components analysis in the paper [4].
See this image and copyright information in PMC

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