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Adaptive two‐stage designs in phase II clinical trials
Author(s) -
Banerjee Anindita,
Tsiatis Anastasios A.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2501
Subject(s) - sample size determination , stage (stratigraphy) , computer science , optimal design , bayesian probability , adaptive design , statistics , null hypothesis , outcome (game theory) , binary number , construct (python library) , mathematical optimization , mathematics , clinical trial , arithmetic , medicine , mathematical economics , paleontology , pathology , biology , programming language
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 or not 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 when the primary outcome is a binary response. This paper proposes an adaptive two‐stage design which 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. Copyright © 2006 John Wiley & Sons, Ltd.