Truncated Cauchy Combination Test: a Robust and Powerful P-value Combination Method with Arbitrary Correlations
By: Bo Chen, Wei Xu, Xin Gao
Potential Business Impact:
Finds hidden patterns in data better than old ways.
Cauchy combination test has been widely used for combining correlated p-values, but it may fail to work under certain scenarios. We propose a truncated Cauchy combination test (TCCT) which focus on combining p-values with arbitrary correlations, and demonstrate that our proposed test solves the limitations of Cauchy combination test and always has higher power. We prove that the tail probability of our test statistic is asymptotically Cauchy distributed, so it is computationally effective to achieve the combined p-value using our proposed TCCT. We show by simulation that our proposed test has accurate type I error rates, and maintain high power when Cauchy combination test fails to work. We finally perform application studies to illustrate the usefulness of our proposed test on GWAS and microbiome sequencing data.
Similar Papers
Evaluating the Cauchy Combination Test for Count Data
Methodology
Finds more real problems in data.
Validity and Power of Heavy-Tailed Combination Tests under Asymptotic Dependence
Statistics Theory
Improves finding weak signals in data.
A Heavily Right Strategy for Integrating Dependent Studies in Any Dimension
Methodology
Combines study results better, making answers more reliable.