Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Yue Wang, Joseph G. Ibrahim, Hongtu Zhu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1109-1119
Number of pages11
JournalBiometrics
Volume76
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • high-dimensional data
  • longitudinal data
  • neuroimaging data
  • survival data

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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