A Bayesian decision approach for sample size determination in phase II trials

Leung, Denis Heng-Yan and Wang, You-Gan (2001) A Bayesian decision approach for sample size determination in phase II trials. Biometrics, 57 1: 309-312. doi:10.1111/j.0006-341X.2001.00309.x

Author Leung, Denis Heng-Yan
Wang, You-Gan
Title A Bayesian decision approach for sample size determination in phase II trials
Journal name Biometrics   Check publisher's open access policy
ISSN 0006-341X
Publication date 2001-03
Sub-type Article (original research)
DOI 10.1111/j.0006-341X.2001.00309.x
Volume 57
Issue 1
Start page 309
End page 312
Total pages 4
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Abstract Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.
Keyword Bayesian
Decision theory
Gain function
Gittins Index
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
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Created: Wed, 17 Nov 2010, 13:53:49 EST