Functional principal component analysis for longitudinal data with informative dropout

2020 ◽  
Vol 40 (3) ◽  
pp. 712-724
Author(s):  
Haolun Shi ◽  
Jianghu Dong ◽  
Liangliang Wang ◽  
Jiguo Cao
Keyword(s):  
Principal Component Analysis ◽  
Longitudinal Data ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Informative Dropout ◽  
Functional Principal Component

Quantile residual lifetime regression with functional principal component analysis of longitudinal data for dynamic prediction

2018 ◽  
Vol 28 (4) ◽  
pp. 1216-1229
Author(s):  
Xiao Lin ◽  
Ruosha Li ◽  
Fangrong Yan ◽  
Tao Lu ◽  
Xuelin Huang
Keyword(s):  
Principal Component Analysis ◽  
Longitudinal Data ◽  
Regression Model ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Residual Lifetime ◽  
Dynamic Prediction ◽  
Functional Principal Component ◽  
Quantile Residual Lifetime

Optimal therapeutic decisions can be made according to disease prognosis, where the residual lifetime is extensively used because of its straightforward interpretation and formula. To predict the residual lifetime in a dynamic manner, a longitudinal biomarker that is repeatedly measured during the post-baseline follow-up period should be included. In this article, we use functional principal component analysis, a powerful and flexible tool, to handle irregularly measured longitudinal data and extract the dominant features over a specific time interval. To capture the time-dependent trajectory pattern, a series of moving time windows are used to estimate window-specific functional principal component analysis scores, which are then combined with a quantile residual lifetime regression model to facilitate dynamic prediction. Estimation of this regression model can be achieved by solving estimating equations with the help of locating the minimizer of the L1-type function. Simulation studies demonstrate the advantages of our proposed method in both calibration and discrimination under various scenarios. The proposed method is applied to data from patients with chronic myeloid leukemia to illustrate its practicality, where we dynamically predict quantile residual lifetimes with longitudinal expression levels of an oncogene, BCR-ABL.

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