Wang Xiaoqing, Song Xinyuan, Zhu Hongtu
Department of Statistics, The Chinese University of Hong Kong, Sha Tin, Hong Kong.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Stat Med. 2021 Feb 20;40(4):920-932. doi: 10.1002/sim.8810. Epub 2020 Nov 10.
Medical imaging data have been widely used in modern health care, particularly in the prognosis, screening, diagnosis, and treatment of various diseases. In this study, we consider a latent factor-on-image (LoI) regression model that regresses a latent factor on ultrahigh dimensional imaging covariates. The latent factor is characterized by multiple manifest variables through a factor analysis model, while the manifest variables are subject to nonignorable missingness. We propose a two-stage approach for statistical inference. At the first stage, an efficient functional principal component analysis method is applied to reduce the dimension and extract useful features/eigenimages. At the second stage, a factor analysis mode is proposed to characterize the latent response variable. Moreover, an LoI model is used to detect influential risk factors, and an exponential tiling model applied to accommodate nonignoreable nonresponses. A fully Bayesian method with an adjust spike-and-slab absolute shrinkage and selection operator (lasso) procedure is developed for the estimation and selection of influential features/eigenimages. Simulation studies show the proposed method exhibits satisfactory performance. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative data set.
医学影像数据已在现代医疗保健中广泛应用，尤其在各种疾病的预后、筛查、诊断和治疗方面。在本研究中，我们考虑一种影像上的潜在因子（LoI）回归模型，该模型将一个潜在因子对超高维影像协变量进行回归。潜在因子通过一个因子分析模型由多个显变量表征，而显变量存在不可忽略的缺失值。我们提出一种两阶段统计推断方法。在第一阶段，应用一种有效的功能主成分分析方法来降维和提取有用特征/特征图像。在第二阶段，提出一个因子分析模型来表征潜在响应变量。此外，使用一个LoI模型来检测有影响的风险因素，并应用一个指数平铺模型来处理不可忽略的无应答情况。为了估计和选择有影响的特征/特征图像，开发了一种带有调整后的尖峰和平板绝对收缩与选择算子（lasso）程序的全贝叶斯方法。模拟研究表明所提出的方法表现出令人满意的性能。所提出的方法应用于阿尔茨海默病神经影像倡议数据集的一项研究。
