@article{4bd2056e819a4426af0c1c748741310b,
title = "Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study",
abstract = "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.",
keywords = "high-dimensional data, longitudinal data, neuroimaging data, survival data",
author = "Yue Wang and Ibrahim, {Joseph G.} and Hongtu Zhu",
note = "Funding Information: Dr. Zhu's work was partially supported by NIH grants R01MH086633 and R01MH116527. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Funding Information: Dr. Zhu's work was partially supported by NIH grants R01MH086633 and R01MH116527. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database ( http://adni.loni.usc.edu ). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp‐content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf . Publisher Copyright: {\textcopyright} 2020 The International Biometric Society",
year = "2020",
month = dec,
doi = "10.1111/biom.13219",
language = "English (US)",
volume = "76",
pages = "1109--1119",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "4",
}