Neural Network on Interval Censored Data with Application to the Prediction of Alzheimer’s Disease-孙韬 讲师（中国人民大学统计学院）
Title: Neural Network on Interval Censored Data with Application to the Prediction of Alzheimer’s Disease
Speaker: 孙韬 讲师（中国人民大学统计学院）
Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles.
报告人简介：孙韬，中国人民大学统计学院讲师，博士毕业于匹兹堡大学生物统计系，主要研究方向为复杂生存数据模型，老年慢性病预防与管理。主持国自然青年基金项目与国家统计局重点项目，学术论文发表于Science, Biometrics, Biostatistics, Statistics in Medicine, Statistical Methods in Medical Research等期刊。担任中国老年学和老年医学学会老龄经济学分会理事。