期刊
BIOMETRICS
卷 57, 期 1, 页码 309-312出版社
INTERNATIONAL BIOMETRIC SOC
DOI: 10.1111/j.0006-341X.2001.00309.x
关键词
Bayesian; decision theory; gain function; Gittins Index; sample size; sequential design
资金
- NCI NIH HHS [CA74207, CA47179] Funding Source: Medline
- NIAID NIH HHS [AI24643] Funding Source: Medline
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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