Department of Biostatistics, University of Washington, Seattle, Washington.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Biometrics. 2020 Dec;76(4):1109-1119. doi: 10.1111/biom.13219. Epub 2020 Feb 3.
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.
许多生物医学研究已经确定了与重复临床测量和生存结果都相关的重要影像学生物标志物。李和罗于 2017 年提出的功能关节模型（FJM）框架，通过基于功能主成分分析（FPCA）的功能偏最小二乘（FPLS）算法，在调整高维图像和低维协变量的同时，研究了重复临床测量和生存数据之间的关联。在本文中，我们提出了一种基于功能偏最小二乘（FPLS）的 FJM 估计的新算法。我们的数值研究表明，与 FPCA 相比，所提出的 FPLS 算法在许多重要情况下可以产生更准确和稳健的估计和预测性能。我们将所提出的 FPLS 算法应用于神经影像学研究。本文准备数据取自阿尔茨海默病神经影像学倡议（ADNI）数据库。
