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Adaptive graph-based multiple testing procedures.

Florian Klinglmueller1, Martin Posch, Franz Koenig

  • 1Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

Pharmaceutical Statistics
|October 17, 2014
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Summary
This summary is machine-generated.

Graph-based multiple testing procedures are generalized for adaptive clinical trial designs with interim analyses. This approach maintains family-wise error rate control without pre-specifying adaptation rules, offering flexibility in complex trial scenarios.

Keywords:
adaptive designgraphical approachmultiple comparisonsmultiple endpointspartial conditional error ratetreatment selection

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Graph-based multiple testing procedures offer intuitive strategies for complex hypothesis relations in clinical trials.
  • Existing methods like gatekeeping and hierarchical tests are special cases of graph-based approaches.
  • Adaptive trial designs allow mid-trial modifications based on interim data and external information.

Purpose of the Study:

  • To generalize graph-based multiple testing procedures for adaptive clinical trial designs.
  • To ensure family-wise error rate control in adaptive designs without detailed pre-specification of adaptation rules.
  • To provide a flexible framework applicable to various complex trial scenarios.

Main Methods:

  • Generalization of graph-based multiple testing procedures to incorporate interim analyses.
  • Development of an adaptive testing procedure that maintains family-wise error rate control.
  • Application of the procedure without requiring knowledge of the multivariate distribution of test statistics.
  • Illustration with a case study and investigation of operating characteristics via simulations.

Main Results:

  • The proposed adaptive test maintains family-wise error rate control.
  • Adaptation rules do not need to be prespecified in detail.
  • The method is broadly applicable to trials with multiple comparisons, endpoints, or subgroups.
  • Adaptive tests reduce to planned procedures if no adaptations are made.

Conclusions:

  • Graph-based multiple testing procedures can be effectively generalized for adaptive clinical trial designs.
  • This approach enhances trial flexibility by allowing data-driven modifications while controlling error rates.
  • The methodology is robust and applicable across diverse and complex clinical trial settings.