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Choosing between methods of combining p-values

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Title: Choosing between methods of combining p-values
Authors: Heard, N
Rubin-Delanchy, P
Item Type: Journal Article
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. A diverse range of p-value combination methods appear in the literature, each with different statistical properties. Yet all too often the final choice used in a meta-analysis can appear arbitrary, as if all effort has been expended 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 of the standard combiners which provide guidance about how a powerful combiner might be chosen in practice.
Issue Date: 1-Mar-2018
Date of Acceptance: 20-Nov-2017
URI: http://hdl.handle.net/10044/1/55807
DOI: https://dx.doi.org/10.1093/biomet/asx076
ISSN: 0006-3444
Publisher: Oxford University Press (OUP)
Start Page: 239
End Page: 246
Journal / Book Title: Biometrika
Volume: 105
Issue: 1
Copyright Statement: © 2018 Biometrika Trust. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biometrika following peer review. The version of record N A Heard, P Rubin-Delanchy; Choosing between methods of combining p p -values, Biometrika, , asx076, is available online at:https://dx.doi.org/10.1093/biomet/asx076
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biology
Mathematical & Computational Biology
Statistics & Probability
Life Sciences & Biomedicine - Other Topics
Mathematics
Edgington's method
Fisher's method
George's method
Meta-analysis
Pearson's method
Stouffer's method
Tippett's method
SAMPLE
stat.ME
0104 Statistics
1403 Econometrics
Publication Status: Published
Open Access location: https://arxiv.org/pdf/1707.06897.pdf
Online Publication Date: 2018-01-04
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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