Optimal Bayesian Design for Patient Selection in a Clinical Study

Authors

  • Manuela Buzoianu,

    Corresponding author
    1. Department of Biostatistics, MedImmune, One MedImmune Way, Gaithersburg, Maryland 20878, U.S.A.
      email: buzoianum@medimmune.com
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    • This work was done while Manuela Buzoianu was at the Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, U.S.A.

  • Joseph B. Kadane

    1. Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, U.S.A.
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email: buzoianum@medimmune.com

Abstract

Summary Bayesian experimental design for a clinical trial involves specifying a utility function that models the purpose of the trial, in this case the selection of patients for a diagnostic test. The best sample of patients is selected by maximizing expected utility. This optimization task poses difficulties due to a high-dimensional discrete design space and, also, to an expected utility formula of high complexity. A simulation-based optimal design method is feasible in this case. In addition, two deterministic algorithms that perform a systematic search over the design space are developed to address the computational issues.

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