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Bayesian Analysis of Semiparametric Cox Models with Latent Variables
发布时间:2018-05-30     点击次数:
报告题目: Bayesian Analysis of Semiparametric Cox Models with Latent Variables
报 告 人: 蔡敬衡 教授 (中山大学)
报告时间: 2018年06月01日 10:30--11:30
报告地点: 数学院二楼报告厅
报告摘要:

 Respiratory cancer is one of the most commonly diagnosed cancers as well as the leading cause of cancer death. Numerous efforts have been devoted to reducing the death rate of respiratory cancer. In this article, we propose a semiparametric Cox model with latent variables to assess the effects of observed and latent risk factors on survival time of respiratory cancer. The characteristics of latent risk factors are characterized via multiple observed indicators by a confirmatory factor analysis model. We develop a Bayesian estimation procedure to obtain the estimates of parameters. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method is applied to analyze the Surveillance, Epidemiology, and End Results (SEER) Program data set.

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