  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 in Li and Luo (2017),
investigates the association between repeated clinical measures and survival
data, while adjusting for both high-dimensional images and low-dimensional
covariates based upon 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 the Alzheimer's Disease Neuroimaging
Initiative (ADNI) study. Data used in the preparation of this article were
obtained from the ADNI database.
