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Approximately optimal designs for phase II clinical studies

N Stallard1

  • 1Medical and Pharmaceutical Statistics Research Unit, The University of Reading, Earley Gate, UK.

Journal of Biopharmaceutical Statistics
|September 19, 1998
PubMed
Summary

Determining sample size for phase II clinical trials remains challenging. This study introduces optimal three-stage designs using decision theory, finding them nearly as effective as other methods but easier to implement.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Decision Theory

Background:

  • Establishing optimal sample size for phase II clinical trials lacks a consensus.
  • Bayesian decision theory offers a framework for sample size determination.

Purpose of the Study:

  • To derive and evaluate optimal three-stage clinical trial designs using decision theory.
  • To compare these designs against existing methods like Schoenfeld, Ensign, Chen, and sequential probability ratio tests.

Main Methods:

  • Optimal three-stage designs were developed using decision theory principles.
  • Performance was assessed by comparing these designs to established sequential and multi-stage procedures.
  • Sensitivity analysis was conducted on the gain function parameters.

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Main Results:

  • The proposed three-stage decision-theory designs closely approximate the true optimal test.
  • The sequential probability ratio test is simpler to implement and only marginally less effective.
  • The performance of decision-theory designs is sensitive to the accurate specification of cost and profit parameters.

Conclusions:

  • Optimal three-stage designs derived from decision theory provide a robust approach to sample size determination in phase II trials.
  • Sequential probability ratio tests offer a practical alternative with minimal loss in optimality.
  • Careful consideration of cost and profit functions is crucial for the successful application of decision-theory-based designs.