报告题目：Factor-Augmented Transformation Models for Interval-Censored Failure Time Data
报告人：李树威（广州大学）
报告时间：2023年4月11日14：00—15:00
报告地点：腾讯会议：663-882-733
摘要:Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical xaminations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This study proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates.
We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event.Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation.We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative study is provided. An R package ICTransCFA is also available for practitioners.
主讲人简介：李树威，广州大学统计系副教授、研究生导师。主要研究领域为生存分析，主持国家级和省部级科研项目，发表学术论文20余篇。
