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An exhaustive ADDIS principle for online FWER control

  • 0Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.
Biometrical Journal. Biometrische Zeitschrift +

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April 18, 2024

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April 18, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces improved online multiple testing methods that enhance hypothesis rejection power while maintaining familywise error rate (FWER) control. These novel procedures uniformly improve upon existing adaptive-discard (ADDIS) methods.

Area Of Science

  • Statistics
  • Statistical hypothesis testing

Background

  • Online multiple testing addresses sequential hypothesis evaluation over time.
  • Familywise error rate (FWER) control is crucial for managing Type I errors in multiple tests.
  • Adaptive-discard (ADDIS) procedures are current state-of-the-art for online FWER control and power.

Purpose Of The Study

  • To uniformly improve the performance of existing ADDIS procedures.
  • To enhance the power of online multiple testing while strictly controlling the FWER.
  • To establish theoretical limits on hypothesis rejection events in FWER-controlled procedures.

Main Methods

  • Development of a novel principle that uniformly improves the ADDIS framework.
  • Theoretical analysis to demonstrate superior or equal rejection rates compared to standard ADDIS.
  • Application of the new principle to specific ADDIS variants like ADDIS-Spending and ADDIS-Graph.

Main Results

  • The proposed methods offer a uniform improvement over all ADDIS procedures, rejecting at least as many hypotheses.
  • In certain scenarios, the new procedures demonstrate increased power for hypothesis rejection.
  • Demonstration that no other FWER controlling procedure can achieve a larger rejection event set.

Conclusions

  • The introduced uniform improvement principle enhances online multiple testing power under FWER control.
  • These advancements provide more effective tools for sequential hypothesis testing in various time-dependent research settings.
  • The findings offer a significant advancement in the field of statistical inference for online data streams.

Keywords:

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