Choosing between methods of combining p-values

N. A. Heard, P. Rubin-Delanchy

Research output: Contribution to journalArticle (Academic Journal)

21 Citations (Scopus)
231 Downloads (Pure)

Abstract

Combining p-values from independent statistical tests is a popular approach to meta-analysis, particularly when the data underlying the tests are either no longer available or are difficult to combine. Numerous p-value combination methods appear in the literature, each with different statistical properties, yet often the final choice used in a meta-analysis can seem arbitrary, as if all effort has been expended in building the models that gave rise to the p-values. Birnbaum (1954) showed that any reasonable p-value combiner must be optimal against some alternative hypothesis. Starting from this perspective and recasting each method of combining p-values as a likelihood ratio test, we present theoretical results for some standard combiners that provide guidance on how a powerful combiner might be chosen in practice.
Original languageEnglish
Pages (from-to)239-246
Number of pages8
JournalBiometrika
Volume105
Issue number1
Early online date4 Jan 2018
DOIs
Publication statusPublished - Mar 2018

Keywords

  • Edgington's method
  • Fisher's method
  • George's method
  • Meta-analysis
  • Pearson's method
  • Stouffer's method
  • Tippett's method

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