Partial Least Squares for Functional Joint Models

TL;DR

This paper introduces a novel functional partial least squares (FPLS) algorithm for estimating functional joint models, improving accuracy and robustness over existing FPCA methods, with applications demonstrated on Alzheimer's disease data.

Contribution

The paper develops a new FPLS-based estimation algorithm for functional joint models, enhancing estimation accuracy and robustness in biomedical imaging studies.

Findings

FPLS outperforms FPCA in estimation accuracy

Improved robustness in prediction performance

Successful application to Alzheimer's disease data

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 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.