Bayesian Adaptive Lasso for Additive Hazard Regression with Current Status Data

发布时间：2019-06-13 作者：浏览次数：

Speaker:	王纯杰	DateTime:	2019年6月17日（周一）下午3：30-4：15
Brief Introduction to Speaker: 王纯杰，长春工业大学基础科学学院，院长，教授。		Place:	六号楼二楼报告厅
Abstract: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 model in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo 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.

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