A Permutation Approach to Testing Interactions for Binary Response by Comparing Correlations Between Classes

Existing methods often have issues with sensitivity to modeling assumptions and heavily asymptotic nominal p -values. To help alleviate these issues, we propose a permutation-based method for testing marginal interactions with a binary response.

Our method searches for pairwise correlations that differ between classes. In this article, we compare our method on real and simulated data to the standard approach of running many pairwise logistic models. On simulated data our method finds more significant interactions at a lower false discovery rate (especially in the presence of main effects).