Program - International Chinese Statistical Association

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SHORT COURSES ICSA 2010 SYMPOSIUM Biometrics, and currently is an associate editor of Clinical Trials and Statistics in Biosciences, serves on several external advisory boards and grant review panels, and is an American Statistical Association Media Expert. Short Course #2: Modern Techniques in Data Mining Instructor: Dr. David Banks, Professor in the Department of Statistical Science at Duke University Location and Time: Studio Five Room, Sunday, June 20, 2010, 9:00 a.m.– 4:30 p.m. Abstract: This one-day short course surveys the development of an important new subfield at the intersection of statistics and computer science. It has important applications in many areas, especially bioinformatics and information technology. The course starts from the perspective of nonparametric regression, addressing the problems of variable selection, local fitting, model assessment and uncertainty, all in the context of the Curse of Dimensionality. Then the course moves to consider comparable issues in the case of classification, cluster analysis, and multidimensional scaling. Finally, the course gives a short summary of three areas of emerging theory that provide insight on when it is possible to make inference in high dimensions, and when there is no really no hope. Key topics include support vector machines, neural networks, boosting and bagging, random forests, overcompleteness, wavelets, sparsity, VC classes, and the LASSO and related methods. About the Instructor: Dr. David Banks is a professor in the Department of Statistical Science at Duke University. Before that, he held positions in the Food and Drug Administration, the Department of Transportation, the National Institute of Standards and Technology, and Carnegie Mellon University. He was editor of the Applications and Case Studies section of the Journal of the American Statistical Association (2007-2009), serves on the Board of the American Statistical Association, and has chaired the ASA Sections on Risk Analysis and Statistics in Defense and National Security. His research focuses on computer-intensive methods, complex data sets, dynamic network models, metabolomics, public policy, and adversarial risk analysis. He has published more than 60 papers, edited six books, and coauthored a monograph. Short Course #3: An Introduction to Propensity Score Methods in Observational Research Instructor: Dr. Peter Austin, Sr. Scientist at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Canada 28
ICSA 2010 SYMPOSIUM SHORT COURSES Location and Time: Concept AB Room, Sunday, June 20, 2010, 8:00 a.m.–12:00 p.m. Abstract: This half-day workshop examines the use of propensity score methods in observational research. Confounding frequently occurs in observational studies of the effects of treatments and exposures on health outcomes. This workshop will address several statistical issues in estimating treatment effects in the presence of confounding. First, a theoretical framework for confounding will be developed and causal diagrams will be introduced. Second, the design of randomized controlled trials (RCTs) will be briefly reviewed. Third, issues in the design of observational studies will be highlighted. We will discuss designing observational studies so that their design mimics some of the characteristics of that of RCTs. Fourth, we will describe design-based and analysis-based methods for removing confounding when estimating treatment effects using observational data. Analysis-based methods include regression-based approaches. Design-based approaches include stratification and propensity score-based methods. Fifth, attendees will be introduced to the concept of the propensity score and how it can be used to remove confounding in observational studies. Issues in propensity score analyses will be covered in more depth. The workshop will cover the following issues in propensity score analyses: specifying the propensity score model; balance diagnostics for assessing the adequacy of the specification of the propensity score model; different propensity score methods (matching, stratification, weighting, and covariate adjustment); and sensitivity analyses for propensity score analyses. About the Instructor: Dr. Peter Austin is a Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Canada and an Associate Professor in the Department of Health Policy, Management and Evaluation at the University of Toronto. His research interests include propensity score methods for causal inference, predictive models for cardiovascular outcomes, statistical methods for provider profiling, and applied Bayesian methods in health services research. He has published actively on propensity score methods and frequently collaborates with investigators in health services research, pharmacoepidemiology, and clinical epidemiology. Short Course #4: Post-Marketing Drug Safety Evaluation Instructor: Dr. A. Lawrence Gould, Merck Research Laboratories Location and Time: Concept CD Room, Sunday, June 20, 2010, 8:00 a.m.–12:00 p.m. Abstract: Surveillance of drug products in the marketplace continues after regulatory approval for a variety of reasons, e.g., to identify rare potential toxicities that are unlikely to have 29
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Program - International Chinese Statistical Association

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