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Related Experiment Videos

Minimization--reducing predictability for multi-centre trials whilst retaining balance within centre.

Sarah Brown1, Helen Thorpe, Kim Hawkins

  • 1Clinical Trials Research Unit, University of Leeds, 17 Springfield Mount, Leeds LS2 9NG, UK. s.brown@leeds.ac.uk

Statistics in Medicine
|December 2, 2005
PubMed
Summary

Minimization in clinical trials can still balance patient groups effectively. Incorporating a random element into minimization reduces predictability while maintaining crucial within-center balance for reliable treatment allocation.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Medical Research Methodology

Background:

  • Minimization is a common patient allocation method in clinical trials.
  • Concerns exist regarding the predictability of treatment allocation with minimization.
  • Large within-center imbalances can create logistical challenges.

Purpose of the Study:

  • To assess predictability and within-center imbalances of minimization methods.
  • To identify the most effective minimization strategy for reducing predictability.
  • To maintain sufficient within-center balance during treatment allocation.

Main Methods:

  • Compared deterministic minimization with minimization incorporating random elements (p=0.95, 0.90, 0.80, 0.75, 0.70).
  • Evaluated prediction rates and within-center imbalances.

Related Experiment Videos

  • Assessed the impact of including or excluding center as a stratification factor.

    • Main Results:

    • Incorporating a random element reduced prediction rates effectively.
    • Random elements minimized increases in within-center imbalance.
    • Excluding center as a stratification factor led to significant within-center imbalance.

    Conclusions:

    • Minimization remains a viable method for treatment allocation in clinical trials.
    • Including center as a stratification factor is crucial for balance.
    • A random element must be incorporated into the minimization algorithm for optimal results.