Inference on Semiparametric Transformation Model with General Interval-censored Failure Time Data
报 告 人:: 王纯杰
报告地点:: 数学与统计楼415室
报告时间:: 2019年01月17日星期四15:45-16:30

Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards models in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo (MCMC) methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.

发 布 人:解悦 发布时间: 2019-01-18