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Multi-arm group sequential designs with a simultaneous stopping rule.

S Urach1, M Posch1

  • 1Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems (CEMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090, Wien, Austria.

Statistics in Medicine
|August 24, 2016
PubMed
Summary
This summary is machine-generated.

Multi-arm group sequential trials offer efficiency. Simultaneous stopping rules, while requiring fewer participants, can be improved with optimized boundaries to enhance power for detecting treatment effects.

Keywords:
closed testingearly stoppingmulti-arm multi-stage designsmultiple comparisonsmultiple treatment arms

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Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Multi-arm group sequential trials efficiently compare multiple treatments against a control.
  • These designs allow interim analyses, potentially reducing overall sample size compared to fixed designs.

Purpose of the Study:

  • To compare simultaneous and separate stopping rules in multi-arm group sequential trials.
  • To investigate improved, less conservative stopping boundaries for the simultaneous stopping rule.

Main Methods:

  • Utilized closed testing procedures for family-wise error rate control.
  • Derived and evaluated improved Pocock and O'Brien type boundaries, alongside optimized boundaries.
  • Investigated operating characteristics and small sample properties of the derived designs.

Main Results:

  • Simultaneous stopping rules require fewer participants to reject at least one null hypothesis compared to separate stopping rules.
  • Simultaneous stopping rules result in lower power to reject all null hypotheses, but this can be partially recovered with improved boundaries.
  • Group sequential boundaries designed for separate stopping also control error rates under simultaneous stopping.

Conclusions:

  • Improved stopping boundaries enhance the efficiency and power of simultaneous stopping rules in multi-arm group sequential trials.
  • The choice of stopping rule impacts the trade-off between sample size and power to reject null hypotheses.
  • The findings offer practical guidance for designing more effective clinical trials.