论文摘要：

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.

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作者介绍：

刘可昕，杜兰大学生物统计系博士研究生，2024年硕士毕业于中国人民大学统计学院。主要研究方向包括生存分析，因果推断等。

祖一宁，中国人民大学统计学院2024届硕士毕业生，主要研究方向为复杂生存数据的分析与应用、流行病学研究等。

易丹辉，中国人民大学教授、博士生导师。研究方向：预测与决策、风险管理与保险、生物医学统计。主持国家自然科学基金、国家社科基金、国家“十一五”科技支撑计划等及企事业委托项目百多项。

丁颖，匹兹堡大学生物统计与健康数据科学系终身教授。研究兴趣涵盖生存分析、大规模多组学数据分析、多重比较方法以及精准医学。她为美国统计协会会士，同时兼任匹兹堡大学研究生院学术事务副院长。

孙韬，通讯作者，中国人民大学统计学院副教授，博士毕业于匹兹堡大学生物统计系。研究方向包括复杂生存数据建模、老年失能失智风险管理、基于神经网络的复杂疾病风险预测。主持国家自然科学基金青年项目和面上项目，全国统计科学研究重点项目。