Searching for significant patterns in stratified data

TL;DR

This paper introduces a new method and algorithm for significant pattern mining in stratified data, effectively accounting for categorical covariates to improve statistical testing in biomedical research.

Contribution

It presents a novel strategy and efficient algorithm for pattern mining that incorporates categorical covariates, addressing limitations of previous testability-based methods.

Findings

Developed an algorithm for stratified pattern mining

Enhanced statistical power in biomedical data analysis

Successfully tested on real-world datasets

Abstract

Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem. Recently, the concept of testability was proposed as one approach to correct for multiple testing in pattern mining while retaining statistical power. Still, these strategies based on testability do not allow one to condition the test of significance on the observed covariates, which severely limits its utility in biomedical applications. Here we propose a strategy and an efficient algorithm to perform significant pattern mining in the presence of categorical covariates with K states.