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Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study
Author(s) -
Wang Yue,
Ibrahim Joseph G.,
Zhu Hongtu
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13219
Subject(s) - neuroimaging , computer science , partial least squares regression , functional principal component analysis , covariate , principal component analysis , alzheimer's disease neuroimaging initiative , artificial intelligence , functional neuroimaging , machine learning , data mining , pattern recognition (psychology) , disease , alzheimer's disease , medicine , psychiatry
Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high‐dimensional images and low‐dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.