JSM 2005 Online Program

Abstract #304320

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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Activity Number:	140
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	General Methodology
Abstract - #304320
Title:	Optimal Two-stage Designs in Phase II Trials
Author(s):	Anindita Banerjee*+ and Anastasios A. Tsiatis
Companies:	North Carolina State University and North Carolina State University
Address:	2309 Champion Court, Raleigh, NC, 27606, United States
Keywords:	two-stage trials ; adaptive design ; optimality criteria ; backward induction ; bayesian decision theory ; simulated annealing
Abstract:	Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective after the accumulation of the data at the end of each stage. Optimal, fixed, two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon (1989) when the primary outcome is a binary response. This paper proposes an adaptive two-stage design that allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion.

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