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A comparison of adaptive allocation rules for group-sequential binary response clinical trials.

Caroline C Morgan1, D Stephen Coad

  • 1Cardinal Systems, 91, avenue de la République, 75011 Paris, France. c.morgan@cardinal-sys.com

Statistics in Medicine
|September 19, 2006
PubMed
Summary

Combining group-sequential and response-adaptive designs in clinical trials can reduce patient numbers and failures. The drop-the-loser rule, applied sequentially, proved most effective for binary response trials.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Clinical trials comparing treatments with dichotomous outcomes can benefit from group-sequential and response-adaptive designs.
  • Group-sequential designs reduce overall patient involvement, while response-adaptive designs minimize assignment to inferior treatments.

Purpose of the Study:

  • To extend sample size re-estimation methods for normally distributed responses to binary response trials.
  • To combine group-sequential and response-adaptive designs for binary outcomes, evaluating different adaptive sampling rules and update frequencies.

Main Methods:

  • Investigated group-sequential designs using log odds ratio and probability of success difference.
  • Studied adaptive sampling rules including drop-the-loser and randomized Pólya urn models, alongside sequential maximum likelihood estimation rules.
  • Explored two methods for integrating adaptive urn designs with group-sequential designs: updating at interim analyses and continually after each response.

Main Results:

  • Simulation results indicated the group-sequential design with the drop-the-loser rule, applied sequentially, was most effective.
  • This combined approach reduced expected treatment failures and average sample size.
  • Nominal error rates were maintained across various success probabilities.

Conclusions:

  • The sequential application of group-sequential designs with the drop-the-loser rule offers an effective strategy for binary response trials.
  • This method optimizes patient allocation, minimizing failures and sample size while preserving statistical integrity.