Statistical Issues in Drug Development
报告人： Mingxiu Hu, Takeda Pharmaceuticals and Yale University
时间：2017-05-11 15:30 ~ 16:30
This presentation will briefly describe the general process of drug development and overview key statistical issues from optimal experimental design and genetic data analysis for drug discovery, innovative designs and advanced clinical data analysis methods for clinical trials, to network meta-analysis and cost-effective analysis in outcomes research after drug approval. The key focuses will be on the roles of statistics in drug development decision making, advanced multiple testing procedures, adaptive clinical trial designs, and statistical methods for gene signature development. Real examples will be used to illustrate the enormous impact statistics can make on patients, medical science, and business.
About the Speaker:
Dr. Mingxiu Hu is Vice President and Global Head of Statistics and Programming at Takeda Pharmaceuticals and Adjunct Professor of Biostatistics at Yale University. He earned his MS degree in Statistics from Peking University, an MA degree in Molecular Biology from Brown University, and a PhD in Biostatistics from George Washington University. He is a Fellow of the American Statistical Association (ASA) and serves on the ASA Board of Directors and the ASA Executive Committee; was a member of the ASA Fellow Committee and the Board of Directors for the International Chinese Statistical Association. His research interest focuses on statistical methodologies and applications for drug development, including innovative clinical trial designs, efficient clinical trial analysis methodologies, Bayesian decision theory, and biomarker enrichment strategies. He has published over 20 scientific articles, edited one book, and co-authored another.