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Utility-based optimization of phase II/III programs.

Marietta Kirchner1, Meinhard Kieser1, Heiko Götte2

  • 1Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany.

Statistics in Medicine
|August 11, 2015
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Summary
This summary is machine-generated.

Optimizing drug development planning involves linking phase II and phase III trials. This study presents methods for determining optimal sample sizes and go/no-go decisions in phase II to maximize success in oncology drug development.

Keywords:
drug developmentexpected utilityoptimizationprobability of successprogram-wise planning

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Area of Science:

  • Pharmacoeconomics
  • Clinical Trial Design
  • Biostatistics

Background:

  • Drug development programs, particularly phase II and phase III trials, represent significant financial investments with high failure rates.
  • Current planning often bases phase III sample size on phase II treatment effects, highlighting the need for integrated planning strategies.
  • Optimizing resource allocation between phase II and phase III is critical for program success.

Purpose of the Study:

  • To develop and present methods for program-wise phase II/III planning in drug development.
  • To determine optimal phase II sample sizes and go/no-go decision rules within a time-to-event framework.
  • To optimize the overall performance of the phase II/III program by considering costs and potential market gains.

Main Methods:

  • Development of methods for integrated phase II/III planning.
  • Application of a utility function incorporating fixed and variable costs and potential market gains.
  • Optimization of phase II sample size and go/no-go decision rules for time-to-event data.
  • Illustration of methods using typical oncology drug development scenarios.

Main Results:

  • The proposed methods enable optimized planning for phase II/III drug development programs.
  • Optimal phase II sample sizes and go/no-go decision rules can be determined to enhance program efficiency.
  • The approach effectively balances development costs with potential market value in oncology.

Conclusions:

  • Integrated planning of phase II and phase III trials is essential for efficient drug development.
  • The presented methods provide a framework for optimizing sample size and decision-making in phase II.
  • This approach can significantly improve resource allocation and success rates in oncology drug development programs.