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Related Experiment Videos

Decision-theoretic designs for dose-finding clinical trials with multiple outcomes.

Shenghua K Fan1, You-Gan Wang

  • 1Department of Statistics and Applied Probability, National University of Singapore, 6 Science Drive 2, Singapore 117546, Singapore. kelly_shfan@yahoo.com

Statistics in Medicine
|October 12, 2005
PubMed
Summary

This study introduces a new framework for sequential dose-finding trials with multiple outcomes. Computationally efficient strategies are developed to approximate optimal treatment selection in clinical trials.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Sequential dose-finding trials are crucial for optimizing drug development.
  • Existing optimal strategies often face computational challenges with multiple outcomes.
  • The continual reassessment method (CRM) is a common but limited approach.

Purpose of the Study:

  • To propose a decision-theoretic framework for sequential dose-finding trials with multiple outcomes.
  • To develop computationally feasible strategies that approximate the optimal solution.
  • To apply and evaluate the framework in Phase I/II clinical trial settings.

Main Methods:

  • A decision-theoretic framework using backward induction for optimal strategy derivation.
  • Development of two computationally compromised strategies, one CRM-related and an improved version.

Related Experiment Videos

  • Application and simulation studies for pediatric HIV and cancer chemotherapy trials.

    • Main Results:

    • The proposed computationally compromised strategies are practical and significantly reduce computational complexity.
    • Simulations demonstrate good performance and ethical patient allocation.
    • The improved strategy offers a better approximation to the optimal solution with increased computation.

    Conclusions:

    • The developed framework offers a practical and ethical approach to sequential dose-finding with multiple outcomes.
    • Computationally compromised strategies provide a viable alternative to computationally prohibitive optimal solutions.
    • The approach shows promise for improving efficiency and patient allocation in early-phase clinical trials.