ArticlePloS one2017
A comparison of multiple testing adjustment methods with block-correlation positively-dependent tests.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Global geodiversity components are not equally represented in UNESCO Global Geoparks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Transfer Learning in Multiple Hypothesis Testing.Entropy (Basel, Switzerland) · 2024Article
- Evolutionarily conserved behavioral plasticity enables context-dependent mating in C. elegans.Current biology : CB · 2023Article
- Permutation-based multiple testing corrections forStatistics in medicine · 2023Article
- Associations of sex-related steroid hormones and proteins with alcohol dependence: A United Kingdom Biobank study.Drug and alcohol dependence · 2023Article
- Elusive hypersomnolence in seasonal affective disorder: actigraphic and self-reported sleep in and out of depressive episodes.Psychological medicine · 2023Article
- Data processing pipeline for cardiogenic shock prediction using machine learning.Frontiers in cardiovascular medicine · 2023Article
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- Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative.Cell genomics · 2022Article
- Phylogeny-guided microbiome OTU-specific association test (POST).Microbiome · 2022Article
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- ApoA-I mimetics reduce systemic and gut inflammation in chronic treated HIV.PLoS pathogens · 2022Article
- Assessing the relationship between physical activity and the gut microbiome in a large, population-based sample of Wisconsin adults.PloS one · 2022Article
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- Phenome-wide screening of GWAS data reveals the complex causal architecture of obesity.Human genetics · 2021Article
- Beyond standard pipeline and p < 0.05 in pathway enrichment analyses.Computational biology and chemistry · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In high dimensional data analysis (such as gene expression, spatial epidemiology, or brain imaging studies), we often test thousands or more hypotheses simultaneously. As the number of tests increases, the chance of observing some statistically significant tests is very high even when all null hypotheses are true. Consequently, we could reach incorrect conclusions regarding the hypotheses. Researchers frequently use multiplicity adjustment methods to control type I error rates-primarily the family-wise error rate (FWER) or the false discovery rate (FDR)-while still desiring high statistical power. In practice, such studies may have dependent test statistics (or p-values) as tests can be dependent on each other. However, some commonly-used multiplicity adjustment methods assume independent tests. We perform a simulation study comparing several of the most common adjustment methods involved in multiple hypothesis testing, under varying degrees of block-correlation positive dependence among tests.
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