Choosing between methods of combining p-values
By N. Heard, Patrick Rubin-Delanchy. 2017
171 citations recorded by the source corpus.
Summary 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.
Subfields: Statistics and Probability
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In Statistics and Probability
- Michael I. Jordan
- Aaditya Ramdas
- Martin J. Wainwright
- Rina Foygel Barber
- Zachary Chase Lipton
- Stephan Günnemann
- Stephan Rabanser
- John C. Duchi
Citations in the corpus
- Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift (2018, methodology): “While a number of less conservative aggregations methods have been proposed [23, 29, 14, 10, 28], we found that even using the Bonferroni method, dimensionality reduction plus aggregation, generally outperformed kernel two-sample testing on those same representations.”
- A unified treatment of multiple testing with prior knowledge using the p-filter (2017, background): “[19] Nicholas A Heard and Patrick Rubin-Delanchy.”