【学术讲堂】Dynamic Survival Prediction using Sparse Longitudinal Images via Multi-dimensional Functional Principal Component Analysis

【专家简介】：石昊伦，西蒙菲莎大学统计与精算系，助理教授。研究方向：贝叶斯模型、临床试验设计、函数型数据分析、医学影像分析等。长期致力于生物统计方法的创新与应用。目前，已在Am Stat， Ann Appl Stat， Bayesian Anal， Biometrics，Biostatistics， Pharm Stat， Stat Med等统计学杂志发表SCI论文25篇， 其中第一作者/通讯作者17篇。主编书籍《临床试验设计的统计方法》由高等教育出版社出版，参编书籍Handbook of Statistical Methods for
Randomized Controlled Trials。

【报告摘要】：Our work is motivated by predicting the progression of Alzheimer's disease (AD) based on a series of longitudinally observed brain scan images.  Existing works on dynamic prediction for AD focus primarily on extracting predictive information from multivariate longitudinal biomarker values or brain imaging data at the baseline; whereas in practice, the subject's brain scan image represented by a multi-dimensional data matrix is collected at each follow-up visit. It is of great interest to predict the progression of AD directly from a series of longitudinally observed images. We propose a novel multi-dimensional functional principal component analysis based on alternating regression on tensor-product B-spline, which circumvents the computational difficulty of doing eigendecomposition, and offers the flexibility of accommodating sparsely and irregularly observed image series. We then use the functional principal component scores as features in the Cox proportional hazards model. We further develop a dynamic prediction framework to provide a personalized prediction that can be updated as new images are collected. Our method extracts visibly interpretable images of the functional principal components and offers an accurate prediction of the conversion to AD. We examine the effectiveness of our method via simulation studies and illustrate its application on the Alzheimer's Disease Neuroimaging Initiative data.

时间：2024年05月21日  10:00 – 11:00

会议地点：位育楼417