Evaluating the Cauchy combination test for count data
Huda Alsulami, Silvia Liverani
TL;DR
This study evaluates the performance of the Cauchy combination test with count data under different correlation structures and finds that it manages type 1 error rates effectively under certain conditions.
Contribution
The novel contribution is evaluating the CCT's performance with correlated count data modeled via copulas and comparing it to existing methods.
Findings
The number of tests, success parameter, and sample size affect the CCT's type 1 error rate.
The CCT better controls type 1 error rates with stronger Gumbel-Hougaard copula correlations.
Copula choice and correlation strength significantly influence error rates for CCT and MinP tests.
Abstract
The Cauchy combination test (CCT) is a p-value combination method used in multiple-hypothesis testing and is robust under dependence structures. This study aims to evaluate the CCT for independent and correlated count data where the individual p-values are derived from tests based on normal approximation to the negative binomial distribution. The correlated count data are modelled via copula methods. The CCT performance is evaluated in a simulation study to assess the type 1 error rate and the statistical power, and compare it with existing methods. Our results indicate that the number of combined tests, the negative binomial success parameter, and sample size significantly affect the type 1 error rate of the CCT under independence or moderate correlation. The CCT has more control over managing the type 1 error rate as the strength increases in the Gumbel-Hougaard copula. In general,…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Statistical Methods and Bayesian Inference · Statistical Distribution Estimation and Applications