Liu Kexin, Zu Yining, Yi Danhui, Ding Ying, Sun Tao
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Stat Med. 2025 Jul;44(15-17):e70190. doi: 10.1002/sim.70190.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder accounting for a significant proportion of global dementia cases. Given the lack of effective treatments, there is growing interest in dynamic prediction methods for timely interventions. Notably, many at-risk individuals with periodic clinic visits provide dynamic cognitive and functional scores. When an individual receives a new score at each follow-up, the dynamic prediction model can integrate the individual's historical scores with the new follow-up scores to offer an updated risk prediction. This study utilizes a comprehensive dataset from the four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, comprising 1702 individuals with multiple time-varying cognitive and functional scores and baseline covariates. We address several challenges: Interval-censored time-to-AD due to intermittent assessments, multiple time-varying covariates, and nonlinear covariate effects on AD development. The proposed approach integrates multivariate functional principal component analysis with a neural network; the former extracts important predictive features from multiple time-varying covariates, while the latter handles the nonlinear covariate effects on interval-censored time-to-AD. This method facilitates individualized and dynamic predictions for AD development. Based on simulation results and application to the ADNI dataset, the proposed method outperforms several other methods in terms of prediction accuracy. Furthermore, it identifies high- and low-risk subgroups with distinct progression risk profiles at each landmark time, enabling early and timely intervention of AD. To facilitate dynamic predictions in practice, we have developed an online prediction platform accessible at http://olap.ruc.edu.cn.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，在全球痴呆病例中占很大比例。鉴于缺乏有效的治疗方法，人们对用于及时干预的动态预测方法越来越感兴趣。值得注意的是，许多定期就诊的高危个体提供了动态认知和功能评分。当个体在每次随访中获得新的评分时，动态预测模型可以将个体的历史评分与新的随访评分相结合，以提供更新的风险预测。本研究利用了阿尔茨海默病神经影像学倡议（ADNI）研究四个阶段的综合数据集，该数据集包含1702名具有多个随时间变化的认知和功能评分以及基线协变量的个体。我们解决了几个挑战：由于间歇性评估导致的AD发病时间间隔删失、多个随时间变化的协变量以及协变量对AD发展的非线性影响。所提出的方法将多元函数主成分分析与神经网络相结合；前者从多个随时间变化的协变量中提取重要的预测特征，而后者处理协变量对间隔删失的AD发病时间的非线性影响。该方法有助于对AD发展进行个性化和动态预测。基于模拟结果以及在ADNI数据集上的应用，所提出的方法在预测准确性方面优于其他几种方法。此外，它在每个标志性时间识别出具有不同进展风险特征的高风险和低风险亚组，从而能够对AD进行早期和及时的干预。为了便于在实践中进行动态预测，我们开发了一个在线预测平台，可通过http://olap.ruc.edu.cn访问。
