Advances in genetics have allowed scientists to identify genes (biomarkers) that are linked with certain diseases. To translate these great scientific findings into real-world products (personalized medicine) for those who need them, clinical trials play an essential and important role. To develop personalized medicine, we need new designs of clinical trials so that genetics information and other biomarkers can be incorporated in treatment selection.
This talk first provides a brief review of design and statistical inference related with personalized medicine. Personalized medicine raises some new challenges for both design and statistical inference of clinical trials due to its complex data structure. A new family of covariate-adaptive designs is proposed for personal medicine. New techniques are introduced to study the theoretical properties of the proposed designs and their corresponding statistical inference. Advantages of the proposed methods are demonstrated through both theoretical and numerical studies. Some further statistical issues are also discussed.
More information about Feifang Hu may be found at http://statistics.columbian.gwu.edu/feifang-hu
This Colloquium is sponsored jointly by the University of Georgia Department of Statistics and the University of Georgia Department of Epidemiology and Biostatistics.