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Preliminary Program Last Updated on Monday, July 7, 2008 02:00:01 EDT Preliminary Program last updated on Monday, July 7, 2008 02:00:01 EDT Add To My Program Thu, 7/31/08, 7:00 PM - 9:00 HY-Granite B-C PM ASA Board of Directors Executive Committee Working Dinner (closed) ASA Board of Directors Chair(s): Peter A. Lachenbruch, Oregon State University Add To My Program Fri, 8/1/08, 7:30 AM - 8:30 HY-Granite A AM ASA Board of Directors Breakfast (closed) ASA Board of Directors Chair(s): Peter A. Lachenbruch, Oregon State University Add To My Program Fri, 8/1/08, 8:30 AM - 6:00 HY-Quartz A-B PM ASA Board of Directors Meeting (closed) ASA Board of Directors Chair(s): Peter A. Lachenbruch, Oregon State University Add To My Program Fri, 8/1/08, 8:30 AM - 5:30 HY-Mineral Hall F-G PM Workshop for Chairs of Programs in Statistics and Biostatistics (closed) ASA Caucus of Academic Representatives Chair(s): Douglas Simpson, University of Illinois at Urbana Champaign Add To My Program Fri, 8/1/08, 12:00 PM - 1:00 HY-Mineral Hall F-G PM Workshop for Chairs of Programs in Statistics and Biostatistics Lunch (closed) ASA Caucus of Academic Representatives Chair(s): Douglas Simpson, University of Illinois at Urbana Champaign Add To My Program Fri, 8/1/08, 12:30 PM - 1:30 HY-Granite A PM ASA Board of Directors Lunch (closed) ASA Board of Directors Chair(s): Peter A. Lachenbruch, Oregon State University Add To My Program Fri, 8/1/08, 6:30 PM - 7:30 HY-Monarch Suite PM JSM Staff and ASA Board of Directors Reception ASA Board of Directors Chair(s): Ron Wasserstein, The American Statistical Association Add To My Program Fri, 8/1/08, 7:00 PM - 9:00 HY-Mineral Hall F-G PM Workshop for Chairs of Programs in Statistics and Biostatistics Dinner (closed) ASA Caucus of Academic Representatives Chair(s): Douglas Simpson, University of Illinois at Urbana Champaign Add To My Program Sat, 8/2/08, 7:00 AM - 6:00 PM CC-A Lobby ASA Membership/Special Assistance/Press Desk Add To My Program Sat, 8/2/08, 7:00 AM - 6:00 PM CC-A Lobby JSM Main Registration Add To My Program Sat, 8/2/08, 7:00 AM - 7:00 PM CC-A Lobby Cyber Center Add To My Program Sat, 8/2/08, 7:30 AM - 8:30 HY-Granite A AM ASA Board of Directors Breakfast (closed) ASA Board of Directors Organizer(s): Peter A. Lachenbruch, Oregon State University Add To My Program Sat, 8/2/08, 8:00 AM - 4:00 PM HY-Agate C Section on Statistical Education Small Group Strategic Planning (closed) Section on Statistical Education Chair(s): Linda J. Young, University of Florida Add To My Program Sat, 8/2/08, 8:00 AM - 5:00 CC-Exhibit Hall F PM Exhibitor Move In and Lounge Add To My Program Sat, 8/2/08, 8:30 AM - 5:30 HY-Mineral Hall F-G PM Workshop for Chairs of Programs in Statistics and Biostatistics (closed) ASA Caucus of Academic Representatives Chair(s): Douglas Simpson, University of Illinois at Urbana Champaign Add To My Program Sat, 8/2/08, 8:30 AM - 6:00 HY-Quartz A-B PM ASA Board of Directors Meeting (closed) ASA Board of Directors Chair(s): Peter A. Lachenbruch, Oregon State University Add To My Program CE_01C Sat, 8/2/08, 8:30 AM - 5:00 PM CC-201 Generalized Linear Mixed Models: Theory and Applications - Continuing Education - Course ASA Instructor(s): Oliver Schabenberger, SAS Institute Inc., Walter Stroup, University of Nebraska-Lincoln This two-day course is for those who want to learn about the theory and application of generalized linear mixed models across disciplines from a non-Bayesian perspective. Each day comprises theory and application components with numerous examples. The material is presented at an applied level, accessible to participants with training in linear statistical models and previous exposure to linear mixed models. On the first day, we will cover classes of mixed models and how their features are made manifest in today's likelihood-based estimation methods. We will make the connection between linear models, generalized linear models, linear mixed models, and generalized linear mixed models (GLMM) in terms of model formulation, distributional properties, and approaches to estimation. Participants will learn that GLMMs are an encompassing family and understand the differences and similarities in approaches to estimation and inference within the family. We will discuss overarching issues that confront analysts who work with correlated, non-normal data, such as overdispersion, the marginal and conditional models, and model diagnostics. During the second day, we will focus on application areas for GLMMs and examples; supporting theory will be introduced as needed. Focus areas will include modeling of rates and proportions, modeling of regular and zero-inflated counts, mixed model smoothing, the computation of power and sample size, and inferential tasks with and without adjustments. Computations will be based on the mixed model tools in SAS/STAT software. Add To My Program CE_02C Sat, 8/2/08, 8:30 AM - 5:00 PM CC-203 Genetic and Microarray Data Analysis - Continuing Education - Course ASA Instructor(s): Russ Wolfinger, SAS Institute Inc., Carl D. Langefeld, Wake Forest University Health Sciences This course is for statisticians who wish to learn about statistical genetics, microarray data analysis, and prediction with genomic biomarkers. Course content will be at the intermediate level. It time permits, we will cover topics such as copy number, exon arrays, ChIP-on-Chip, and eQTL. There will be a mixture of theory and practical examples. JMP Genomics software and custom scripts will be used for illustration. Add To My Program CE_03C Sat, 8/2/08, 8:30 AM - 5:00 PM CC-204 Optimal Experimental Designs - Continuing Education - Course ASA, Section on Physical and Engineering Sciences Instructor(s): Alexander N. Donev, University of Manchester, Randy Tobias, SAS Institute Inc. Optimal design for the practitioner is often discussed as a "black box," shying away from the theory. On the contrary, the premise for this course is that powerful practical approaches for assessing the properties of standard designs and of finding good designs in nonstandard situations result from familiarity with the theory of optimal experimental design. We will start by covering fundamental theory, including forms of the General Equivalence Theorem that are central to algorithms for the construction of optimal designs. These ideas will be illustrated with standard designs for response surface models. We will move on to common nonstandard problems in design for response surfaces, such as blocking, finding designs over irregular regions, and mixture designs. We will also discuss the augmentation of designs and designs for checking the adequacy of models. Many models in chemistry and the pharmaceutical industry are nonlinear in the parameters. Optimal designs for these models depend on prior information about the parameters, which may be available in the form of a prior distribution. We will show how this information may be used to provide good designs. Add To My Program CE_04C Sat, 8/2/08, 8:30 AM - 5:00 PM CC-205 Regression Modeling Strategies - Continuing Education - Course ASA Instructor(s): Frank E. Harrell, Jr., Vanderbilt University All standard regression models have assumptions that must be verified for the model to have power to test hypotheses and predict accurately. Of the principal assumptions, this course will emphasize methods for assessing and satisfying linearity and additivity. Practical but powerful tools will be presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response by augmenting the design matrix using restricted cubic splines. Even when assumptions are satisfied, over fitting can ruin a model's predictive ability for future observations. Methods for data reduction will be introduced, methods of model validation will be covered, and auxiliary topics such as modeling interaction surfaces, efficiently utilizing partial covariable data by using multiple imputation, variable selection, overly influential observations, collinearity, and shrinkage will be discussed. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, and survival models. Add To My Program CE_05C Sat, 8/2/08, 8:30 AM - 5:00 CC-210-212 PM Hot Topics in Clinical Trials - Continuing Education - Course ASA, Section on Teaching Statistics in the Health Sciences, ASA, Boston Chapter Instructor(s): Scott R. Evans, Harvard University, Lee-Jen Wei, Harvard University, Lu Tian, Northwestern University, Lingling Li, Harvard Medical School We will address several hot-topic areas in clinical trials, including the use of prediction to identify biomarkers, meta-analysis of rare safety events, data monitoring committees, data monitoring using prediction, noninferiority studies, causal inference, benefit:risk assessment, and bridging studies. We will present motivating examples and discuss standard and novel approaches to analyses. Add To My Program CE_06C Sat, 8/2/08, 8:30 AM - 5:00 PM CC-207 Successful Data Mining in Practice - Continuing Education - Course ASA Instructor(s): Richard D. De Veaux, Williams College This course will introduce data mining, which is the exploration and analysis of large data sets by automatic or semiautomatic means with the purpose of discovering meaningful patterns. The knowledge learned from these patterns can be used for decisionmaking via "knowledge discovery." Much exploratory data analysis and inferential statistics concern the same type of problems, so what is different about data mining? What is similar? In the course, I will attempt to answer these questions by providing a broad survey of the problems that motivate data mining and the approaches used to solve them.
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