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A Bayesian Decision Approach for Sample Size Determination in Phase II Trials
Author(s) -
Leung Denis HengYan,
Wang YouGan
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00309.x
Subject(s) - bayesian probability , sample size determination , statistics , phase (matter) , automatic gain control , biometrics , computer science , optimal design , word error rate , mathematics , artificial intelligence , telecommunications , physics , amplifier , bandwidth (computing) , quantum mechanics
Summary. 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.

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