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Summary

Permutation tests control type I error rates, ensuring validity even with data adaptations. This study connects permutation and t-tests, explaining why adaptations are valid for both, including blinded sample size recalculation.

Keywords:
adaptive methods in clinical trialsasymptotic distributionblinded sample size recalculationcomplete sufficient statisticp-value combination functionspermutation tests

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Area of Science:

  • Biostatistics
  • Statistical Inference
  • Clinical Trial Design

Background:

  • Permutation tests offer a robust method for hypothesis testing by randomizing treatment assignments.
  • Controlling the conditional type I error rate ensures test validity, even with adaptive strategies.
  • Understanding the relationship between permutation and t-tests is crucial for statistical practice.

Purpose of the Study:

  • To demonstrate the validity of permutation tests with data adaptations.
  • To elucidate the connection between permutation tests and t-tests.
  • To explain the rationale behind valid adaptations in t-tests, using blinded sample size recalculation as an example.

Main Methods:

  • Conditional permutation testing framework.
  • Comparative analysis of permutation and t-tests.
  • Illustrative example using blinded sample size recalculation.

Main Results:

  • Permutation tests maintain controlled conditional type I error rates under data adaptations.
  • A clear mathematical and conceptual link is established between permutation and t-tests.
  • The validity of specific adaptations, such as blinded sample size recalculation, is explained through this connection.

Conclusions:

  • Permutation tests provide a valid framework for hypothesis testing, accommodating data-driven adaptations.
  • The established connection between permutation and t-tests enhances understanding and application of statistical methods in clinical research.
  • Blinded sample size recalculation is a valid adaptation within this framework, preserving test integrity.