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A Sample-Size-Optimal Bayesian Procedure for Sequential Pharmaceutical Trials
Noel Cressie and Jonathan Biele
Vol. 50, No. 3 (Sep., 1994), pp. 700-711
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2532784
Page Count: 12
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Consider a pharmaceutical trial where the consequences of different decisions are expressed on a financial scale. The efficacy of the new drug under consideration has a prior distribution obtained from the underlying biological process, animal experiments, clinical experience, and so forth. Berry and Ho (Biometrics 44, 219-227) show how these components are used to establish an optimal (Bayes) sequential testing procedure, assuming a known constant sample size at each decision point. We show in this article how it is also possible to optimize further, with respect to the sample-size rule. This last component of the design, which is missing from most sequential procedures, has the potential to yield considerably larger expected net gains (equivalently, considerably smaller Bayes risks).
Biometrics © 1994 International Biometric Society