JSM 2005 Online Program

JSM Activity #CE_02C

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

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CE_02C	Sat, 8/6/05, 8:00 AM - 4:00 PM	MCC-101 G
Bayesian Clinical Trial Design - Approaches and Implementation - Continuing Education - Course
ASA, Section on Bayesian Statistical Science, Biopharmaceutical Section
Instructor(s): Peter Mueller, The University of Texas M. D. Anderson Cancer Center, J. Jack Lee, The University of Texas M. D. Anderson Cancer Center
This one-day short course will consist of eight lectures, each lasting approximately 45 minutes. The lectures will cover aspects of Bayesian inference related to the design and analysis of clinical trials. The lectures are planned as a mix of review of basic theory and concepts and a discussion of practical trial designs. This is a basic introductory course. We will start with a review of the Bayesian paradigm and how it applies to decision making in general, and clinical trials in particular. We will then proceed to discuss Bayesian clinical trial design for early phase trials, including inference about the maximum tolerable dose, dose finding and adaptive allocation. We will review designs based on tracking posterior probabilities of clinically meaningful events, designs based on inference loss, and designs based on a formal decision theoretic formulation of the trial goals. Time permitting we will discuss sequential algorithms, reviewing practical implementations of backward induction and a setup of drug discovery as a sequential process. Examples will be drawn from actual trials at M.D. Anderson Cancer Center and will be naturally focused on cancer trials. The course targets students or professionals with a good knowledge of statistics and some familiarity with the practice of clinical trial design, who want to learn the basics of Bayesian clinical trial design. The level of mathematical sophistication will be kept as low as possible. Calculus, basic probability, familiarity with statistical inference and the clinical trial setup are assumed.