主 题：Model-based Inference for Subgroup Analysis
主讲人：沈娟博士
主持人：林华珍教授
时 间：2014年10月24日17：10-17：50
地 点：通博楼B座212学术会议室
主办单位：统计学院 统计研究中心 科研处
主讲人简介：
沈娟博士毕业于中国科学技术大学 （学士），美国伊利诺伊大学（硕士）和美国密歇根大学（博士）。现任复旦大学管理学院统计学系助理教授。她的主要研究方向为混合模型，亚组分析，和贝耶斯变量选择。
内容提要：
Subgroup analysis is an important problem in clinical trials. For example, when a new treatment is approved for use, there may be concerns that the efficacy is driven by extreme efficacy in a subgroup only. In recent years, researchers often attempt to identify a potential subgroup with an enhanced treatment effect. In this project, we assume that there exist two potential subgroups in which the subjects react differently to the treatment. We propose a logistic-normal mixture model where the group means as well as the mixing proportions may be covariate-dependent. Testing the existence of subgroups is critical in the mixture model, but requires nonstandard statistical tests. We derive a test based on a small number of EM iterations towards the likelihood, and propose the bootstrap approximation for the critical values of the test. When subgroups exist, the mixture model helps us identify the factors that are associated with the group membership. We apply the proposed method to the Aids Clinical Trials Group 320 study, and demonstrate that the patients with higher values of baseline CD4 or RNA tend to benefit significantly more by adding a protease inhibitor to two nucleoside analogues.
