讲座题目: Semiparametric transformation models for semicompeting survival data
主讲人: 西南财经大学统计学院统计研究中心主任 林华珍教授
主讲人简介:
林华珍，华西医科大学卫生统计专业博士，美国华盛顿老员工物统计系博士后，西南财经大学统计学院教授。先后作为副研究员、访问教授或访问学者在美国UCLA、美国University of Washington、加拿大University of Waterloo、英国University of Manchester、香港中文大学、香港大学、香港科技大学及香港浸会大学学习和工作。
林教授2011年获国家杰出青年科学基金资助，2010入选教育部新世纪优秀人才支持计划。从事统计学研究，论文发表在包括《The Annals of Statistics》、《Journal of the Royal Statistical Society Series B》、《Biometrika》及《Biometrics》国际统计学排位前5的学术刊物上。
时间：2014年5月21日下午15:00点
地点：伟德bevictor中文版犀浦校区bevictor伟德官网会议室X2511
内容简介:
Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly collected in clinical trials. However, analysis of these data is often hampered by a scarcity of available statistical tools. As such, we propose a novel semiparametric transformation model that improves the existing models in the following ways. First, it estimates regression coefficients and association parameters simultaneously. Second, the measure of surrogacy, for example, the proportion of the treatment effect that is mediated by the surrogate and the ratio of the overall treatment effect on the true end point over that on the surrogate end point, can be directly obtained. We propose a two-stage estimation procedure for inference and show that the proposed estimator is consistent and asymptotically normal. Extensive simulations demonstrate the valid usage of our method. We apply the method to a multiple myeloma trial to study the impact of several biomarkers on patients' semicompeting outcomes, namely, time to progression and time to death.
