主 题：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
主持人：西南财经大学保险学院 陈滔院长
时 间：2014年4月22日（周二）14:00—16:00
地 点：柳林校区颐德楼 H311
主办单位：保险学院 科研处
主讲人简介：
林华珍，国际概率论与数理统计学会中国分会(IMS-China)委员(08-10年，10-12年)；《应用概率统计》第七届编委；第九届中国概率统计学会理事.曾在四川大学数学系获学士学位，四川大学数学系获硕士学位，华西医科大学公共卫生学院获博士学位，美国华盛顿大学生物统计系获博士后学位。并先后作为学者，教授，研究员访问香港大学，香港科技大学，美国加州大学洛杉矶分校等知名高校。
林华珍教授在非参数理论和方法，转换模型，相关数据分析等领域获得巨大成就。她申请的项目是全国财经院校中首次获得国家杰出青年科学基金立项，也是全国统计学界第五次获得该类项目的资助。
内容提要：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.
