ArticleScientific reports2022
Robust tests for combining p-values under arbitrary dependency structures.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.Bioinformatics (Oxford, England) · 2026Article
- metacp: a versatile software package for combining dependent or independent p-values.BMC bioinformatics · 2025Article
- Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues.Journal of biological engineering · 2023Article
- Cauchy combination omnibus test for normality.PloS one · 2023Article
- Article
- Robust tests for combining p-values under arbitrary dependency structures.Scientific reports · 2022Article
- The impact of violating the independence assumption in meta-analysis on biomarker discovery.Frontiers in genetics · 2022Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Recently Liu and Xie proposed a p-value combination test based on the Cauchy distribution (CCT). They showed that when the significance levels are small, CCT can control type I error rate and the resulting p-value can be simply approximated using a Cauchy distribution. One very special and attractive property of CCT is that it is applicable to situations where the p-values to be combined are dependent. However, in this paper, we show that under some conditions the commonly used MinP test is much more powerful than CCT. In addition, under some other situations, CCT is powerless at all. Therefore, we should use CCT with caution. We also proposed new robust p-value combination tests using a second MinP/CCT to combine the dependent p-values obtained from CCT and MinP applied to the original p-values. We call the new tests MinP-CCT-MinP (MCM) and CCT-MinP-CCT (CMC). We study the performance of the new tests by comparing them with CCT and MinP using comprehensive simulation study. Our study shows that the proposed tests, MCM and CMC, are robust and powerful under many conditions, and can be considered as alternatives of CCT or MinP.
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