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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Updated: May 15, 2026

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
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Memory and other properties of multiple test procedures generated by entangled graphs.

Willi Maurer1, Frank Bretz

  • 1Novartis Pharma AG, Basel, Switzerland.

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|January 3, 2013
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Summary

New graphical methods for clinical trials enhance hypothesis testing by introducing memory into significance level propagation. Entangled graphs represent hypothesis importance and control error rates effectively.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Addressing multiplicity is crucial in clinical trials.
  • Sequentially rejective graphical procedures visualize complex testing strategies.
  • Current graphical methods lack memory, ignoring the origin of significance levels.

Purpose of the Study:

  • To introduce memory into sequentially rejective graphical procedures.
  • To reflect the dependence structure of study objectives in hypothesis testing.
  • To develop methods for representing relative importance between hypotheses.

Main Methods:

  • Utilizing sequentially rejective graphical procedures.
  • Representing hypotheses as weighted vertices and propagation rules as directed edges.
  • Inducing memory and other properties via convex combination of individual graphs.

Main Results:

  • Developed 'entangled graphs' that incorporate memory.
  • Demonstrated intuitive representation of hypothesis importance relationships.
  • Confirmed that entangled graphs remain sequentially rejective and control familywise error rate.

Conclusions:

  • Entangled graphs offer an intuitive and effective way to manage complex hypotheses in clinical trials.
  • These methods enhance statistical power while maintaining strong error rate control.
  • The introduced memory feature better reflects real-world clinical trial dependencies.