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A Sample-Size-Optimal Bayesian Procedure for Sequential Pharmaceutical Trials

Noel Cressie and Jonathan Biele
Biometrics
Vol. 50, No. 3 (Sep., 1994), pp. 700-711
DOI: 10.2307/2532784
Stable URL: http://www.jstor.org/stable/2532784
Page Count: 12
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A Sample-Size-Optimal Bayesian Procedure for Sequential Pharmaceutical Trials
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Abstract

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).

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