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Sample sizes for phase II clinical trials derived from Bayesian decision theory.

H C Brunier·J Whitehead
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Abstract

In early phase clinical trials of a new medical treatment, patients are treated to decide whether there is sufficient promise to justify additional studies. A decision theoretic approach is proposed to help determine the number of patients that should be treated. The optimal sample size is obtained by maximizing a utility function which incorporates both the number of 'gained successes' and the costs of treatment. The method extends work of Sylvester and Staquet, and adopts a Bayesian formulation. Numbers of patients in later studies and in eventual routine use of the treatment are taken into account. We allow for the possibility that a later study might lead to an erroneous conclusion. The effects of these various influences on the recommended sampling plan for the early phase clinical trial are explored.

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Bayes TheoremChi-Square DistributionClinical Trials, Phase II as TopicCosts and Cost AnalysisDecision TheoryHumansSample SizeTreatment OutcomeJournal Article

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H C Brunier, J Whitehead. Sample sizes for phase II clinical trials derived from Bayesian decision theory.. https://doi.org/10.1002/sim.4780132312

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