Feng Jiahui, Shi Haolun, Ma Da, Faisal Beg Mirza, Cao Jiguo
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.
School of Medicine, Wake Forest University, Winston-Salem, North Carolina, USA.
Stat Med. 2025 Feb 10;44(3-4):e10324. doi: 10.1002/sim.10324.
Brain imaging data is one of the primary predictors for assessing the risk of Alzheimer's disease (AD). This study aims to extract image-based features associated with the possibly right-censored time-to-event outcomes and to improve predictive performance. While the functional proportional hazards model is well-studied in the literature, these studies often do not consider the existence of patients who have a very low risk and are approximately insusceptible to AD. We introduce a functional mixture cure rate model that extends the proportional hazards model by allowing a proportion of event-free patients. We propose a novel supervised functional principal component analysis (sFPCA) method to extract image features associated with AD risk while accounting for the complexity arising from right censoring. The proposed method accommodates the irregular boundary issue inherent in brain images with bivariate splines over triangulations. We demonstrate the advantages of the proposed method through extensive simulation studies and provide an application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
脑成像数据是评估阿尔茨海默病（AD）风险的主要预测指标之一。本研究旨在提取与可能存在右删失的事件发生时间结局相关的基于图像的特征，并提高预测性能。虽然文献中对功能比例风险模型进行了充分研究，但这些研究通常没有考虑到存在极低风险且几乎不易患AD的患者。我们引入了一种功能混合治愈概率模型，该模型通过允许一部分无事件患者来扩展比例风险模型。我们提出了一种新颖的监督功能主成分分析（sFPCA）方法，以提取与AD风险相关的图像特征，同时考虑右删失带来的复杂性。所提出的方法通过三角剖分上的双变量样条来处理脑图像中固有的不规则边界问题。我们通过广泛的模拟研究证明了所提出方法的优势，并将其应用于阿尔茨海默病神经影像倡议（ADNI）研究。
