Date: Tuesday, June 21st, 14:00--15:00
Location:B212, Tong Bo Building, Liu Lin Campus
The public lecture
Professor: Ming Tony Tan
Georgetown University Medical Center
About the lecturer:
Dr. Tan is professor and chair of the Department of Biostatistics, Bioinformatics and Biomathematics at Georgetown University Medical Center/Lombardi Comprehensive Cancer Center (http://dbbb.georgetown.edu). He came Georgetown in 2012 from University of Maryland School of Medicine and the University of Maryland Marlene and Stewart Greenebaum Cancer Center, where had been Professor and director of biostatistics since 2002. He was previously a senior member (faculty) at St. Jude Children's Research Hospital Cancer Center and biostatistics director of St Jude's Developmental Therapeutics for Solid Malignancies Program (1997-2002), assistant and associate staff/professor of Biostatistics and Epidemiology at The Cleveland Clinic (1990-1997). He received his Ph.D. in Statistics in 1990 from Purdue University, Indiana.
Dr. Tan’s research covers the design, monitoring and analysis of clinical trials (in both multi-center and single institutional settings), laboratory investigations, biomarker evaluation, genomics and epidemiological research. His current research focuses on developing statistical methods for multidrug combinations utilizing experimental data, pharmacology, system biology and modern statistical theory, innovative methods to design and efficiently analyze clinical trials incorporating multiple genomic markers; and bioinformatics approaches for high dimensional genomics data in Cancer Epidemiology, all funded by the NCI and NHLBI.
Dr. Tan has served on multiple NIH study sections (such as Clinical Oncology and Epidemiology of Cancer), review and site visit panels (such as the P30, P50, and P01), Data and Safety Monitoring Boards, and has been a member of FDA Advisory Committee. He is a Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. Dr. Tan is current Associate Editor of Statistics in Medicine and Drug Design, Development and Therapy, and a senior editor of Journal of Clinical and Translational Science. He has more than 180 peer reviewed papers.
We propose a robust method via a semiparametric model to test if sub-groups with differential treatment effects in clinical trials exist and to identify such subgroups. The model is formulated as a geometrical mean of a parametric and a nonparametric component. The former represents our knowledge about the model and the latter represents the uncertainty. The profile likelihood method 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. Both the properties of the procedure is studied analytically with proofs and by simulation. This work is in collaboration with Dr. A. Yuan.
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