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Combining exchangeable P-values.

Matteo Gasparin1, Ruodu Wang2, Aaditya Ramdas3

  • 1Department of Statistical Sciences, University of Padova, Padova 35121, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|March 14, 2025
PubMed
Summary
This summary is machine-generated.

Combining P-values, especially when dependent, is improved using exchangeability or randomization. New methods enhance existing rules for P-value combination, offering better statistical power.

Keywords:
dependent P-valuese-valuesglobal null testingmultiple testingrandomization

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Area of Science:

  • Statistical inference
  • Hypothesis testing

Background:

  • Combining P-values is crucial in statistical analysis.
  • Independence assumption is often violated in real-world applications.

Purpose of the Study:

  • To develop improved methods for combining dependent P-values.
  • To enhance existing P-value combination rules.

Main Methods:

  • Utilizing exchangeability and external randomization for P-value combination.
  • Deriving randomized and exchangeable improvements of established rules (e.g., median, average, geometric, harmonic means).
  • Calibrating P-values to e-values, averaging e-values, and applying Markov's inequality variants.

Main Results:

  • Demonstrated strict improvements over existing P-value combination rules under exchangeability or randomization.
  • Developed sequential combination methods for P-values produced over time.
  • Showed that arbitrary dependent P-values become exchangeable when randomly ordered.

Conclusions:

  • Exchangeability and randomization offer significant improvements for P-value combination.
  • New methods provide enhanced statistical power and flexibility in hypothesis testing.
  • The findings advance the field of meta-analysis and combining evidence from multiple studies.