The ASA recently chose the final set of short courses for JSM 2010, and, among those, the Section on Bayesian Statistical Science (SBSS) is sponsoring the following three:

Bayesian Adaptive Methods for Clinical Trials, presented by Brad Carlin of the University of Minnesota, Don Berry and Jack Lee of M.D. Anderson Cancer Center, and Scott Berry of Berry Consultants—This course introduces hierarchical Bayes methods for the design, interim monitoring, and analysis of clinical trials data and demonstrates their usefulness in challenging applied settings. Methods appropriate for phases I, II, and III of the American regulatory system will be covered. Illustrations using the R, WinBUGS, and BRugs software packages also will be provided.

Bayesian Ecology: Hierarchical Modeling for Ecological Processes, presented by James Clark and Alan Gelfand of Duke University—This course will present, in substantial detail with data analysis, four illustrative process modeling contexts: forest dynamics, species distributions and biodiversity, pathogens on hosts, and species diffusion processes.

Monte Carlo and Bayesian Computation with R (cosponsored with the Section on Physical and Engineering Sciences), presented by Jim Albert and Maria Rizzo of Bowling Green State University—R tools will be described for generating random variables, computing criteria of statistical procedures, and replicating the procedure to compute quantities such as mean squared error and probability of coverage. R commands for implementing simulation-based procedures such as bootstrap and permutation tests will be outlined. The use of R in Bayesian computation will be described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms.

SBSS also will sponsor the following roundtables:

Decisionmaking in Public Policy: Problems from DOT, FDA, and NASA, led by David Banks of Duke University —This roundtable will focus on how Bayesian methods have been embraced, ignored, or gingerly poked at by a number of federal agencies in the context of many specific policy applications.

Bayesian Modeling for Space-Time Surveillance of Disease, led by Andrew Lawson of the Medical University of South Carolina—This discussion will focus on the difference between space-time retrospective modeling of disease variation and modeling for surveillance purposes. Alternatives also will be discussed.

Bayesian Methods in Genomics: Searching for Unity in Diverse Data Sources, led by Bhramar Mukharjee of the University of Michigan—During this roundtable, we will focus on recent important Bayesian applications in genomics and identify emerging areas where involvement of Bayesian researchers may be beneficial.

Meta-Analysis: Current State, Recent Developments, and Unresolved Problems, led by Dalene Stangl, Duke University—This roundtable invites participants to discuss the current state, recent developments, and unresolved problems in meta-analysis. Participants are encouraged to bring ideas and questions from their own work.