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美国Georgetown University 生物统计和生物信息系首席教授谭铭教授学术报告

来源:统计系  作者:统计系     日期:2017/12/13 20:35:21   点击数:239  
 

报告题目:Robust Subgroup Analysis and Precision Medicine

Ming T. Tan, Professor, Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington DC.

报告人:谭铭教授,美国Georgetown University 生物统计和生物信息系首席教授,发表高水平学术论文共190余篇,其中包括Nature Medicine, Lancet Oncology, Biometrics, Statistics in Medicine, Statistical Methods in Medical Research等。任BiometricsStatistics in Medicine 副主编。

报告时间: 20171221日,上午10:30AM,

报告地址: X2511,

主持人:杨宝莹教授

摘要:

Subgroup analyses have been an important part of the analysis of clinical trial. However, they are commonly over-interpreted and can lead to further misguided research or worse to suboptimal patient care. Today Big Data including molecular and cellular markers only make the problem worse, which is one of the big challenges in converting the vision of precision medicine into reality. In this talk, I will first present our findings on subgroup identification with omics data using novel statistical learning and functional models. Then I will propose a robust method via a semiparametric model to formally test if sub-groups with differential treatment effects in clinical trials exist and if so to classify subjects into subgroups, a critical issue in precision medicine. In more general terms, the method applies to testing if there is a group of subjects with differential outcome. The model is formulated as a geometrical mean of a parametric and a nonparametric component with the mixture proportion as a parameter to be estimated from the data. The profile likelihood approach is used for model estimation. The profile likelihood ratio and score statistics are used to test the existence of subgroups. If existence of subgroups is confirmed, we use Neyman-Pearson rule to classify each subjects to one of the subgroups, so that the misclassification error for the treatment favored group is controlled by pre-specified criterion and is minimized for the other subgroup. The large sample properties of the procedure are derived and finite sample performance is studied by simulation. The work is in collaboration with Drs. A. Yuan, H Fang, Z Liu, Z Gao and B. Wang.

 

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