Stallard (1998, Biometrics54, 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, Biometrics50, 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.
Bayesian, Decision theory, Gain function, Gittins Index, Sample size, Sequential design
Econometrics | Medicine and Health Sciences
LEUNG, Denis H. Y. and WANG, You-Gan.
A Bayesian Decision Approach for Sample Size Determination in Phase II Trials. (2001). Biometrics. 57, (1), 309-312. Research Collection School Of Economics.
Available at: https://ink.library.smu.edu.sg/soe_research/32
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