American Statistical Association
In this talk, we discuss a number of issues in discovering and evaluating personalized medicine using clinical trials. We discuss and propose various methods for estimating optimal treatment regimens of both static and dynamic types. These regimens are functions of high dimensional patient variables, including biomarkers and other prognostic data. Various artificial intelligence methodologies are utilized, including support vector machines, support vector regression, reinforcement learning, Q-learning, and others. We discuss applications in non-small cell lung cancer, colorectal cancer, and in cystic fibrosis. We also briefly discuss several open statistical questions.
Michael R. Kosorok, Ph.D., is Professor and Chair of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill. His research areas include biostatistics, statistical learning, high dimensional data, empirical processes, semiparametric inference, survival analysis, clinical trials, personalized medicine, cancer and cystic fibrosis. He has authored over 100 peer-reviewed scientific publications, including a book on empirical processes and semiparametric inference published with Springer in 2008. He is Associate Editor for both the Journal of the American Statistical Association (Theory and Methods) and the Annals of Statistics and is an honorary fellow of both the American Statistical Association and the Institute of Mathematical Statistics.
|Date:||Thursday, April 28, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York