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Combining p-values in replicated single-case experiments with multivariate outcome.

Francesca Solmi1, Patrick Onghena

  • 1a Interuniversity Institute for Biostatistics and Statistical Bioinformatics , University of Hasselt , Belgium.

Neuropsychological Rehabilitation
|March 6, 2014
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Summary
This summary is machine-generated.

This study explores combining p-values using permutation theory for statistical analysis. It demonstrates this method

Keywords:
Nonparametric combinationPermutation testingProbabilities combinationSingle-case experiments

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Area of Science:

  • Statistics
  • Statistical Methods
  • Biostatistics

Background:

  • Combining probabilities has a long history in statistics, initiated by Ronald Fisher.
  • Combining p-values offers a global decision rule for multiple independent tests.
  • Permutation theory provides an effective approach for combining p-values.

Purpose of the Study:

  • To provide an overview of the concept of combining p-values.
  • To explain how permutation techniques can be used to combine p-values.
  • To apply the method of combining p-values to replicated single-case experiments.

Main Methods:

  • Exploiting permutation distributions of independent tests.
  • Utilizing a simple function to combine probabilities (p-values).
  • Applying permutation techniques for combining results in single-case experiments.

Main Results:

  • The paper presents a method for combining p-values via permutation techniques.
  • The approach is particularly useful for analyzing replicated single-case experiments.
  • A numerical illustration of the method applied to simulated data is provided.

Conclusions:

  • Combining p-values using permutation theory is a viable statistical approach.
  • This method enhances the analysis of complex experimental designs like single-case studies.
  • The study demonstrates the practical application and utility of combining p-values.