Quantile Graph Discovery through QuACC: Quantile Association via Conditional Concordance

TL;DR

This paper introduces QuACC, a novel method for detecting quantile-specific associations in graphical models, improving sensitivity to tail dependencies in biomedical data analysis.

Contribution

The paper presents QuACC, a new measure of quantile-specific association, and develops a class of graphical models that detect tail-dependent relationships.

Findings

QuACC effectively detects tail-specific dependencies in simulations.

The method uncovers quantile-specific patterns in biobank data.

QuACC outperforms traditional methods in identifying tail associations.

Abstract

Graphical structure learning is an effective way to assess and visualize cross-biomarker dependencies in biomedical settings. Standard approaches to estimating graphs rely on conditional independence tests that may not be sensitive to associations that manifest at the tails of joint distributions, i.e., they may miss connections among variables that exhibit associations mainly at lower or upper quantiles. In this work, we propose a novel measure of quantile-specific conditional association called QuACC: Quantile Association via Conditional Concordance. For a pair of variables and a conditioning set, QuACC quantifies agreement between the residuals from two quantile regression models, which may be linear or more complex, e.g., quantile forests. Using this measure as the basis for a test of null (quantile) association, we introduce a new class of quantile-specific graphical models. Through simulation we show our method is powerful for detecting dependencies under dependencies that manifest at the tails of distributions. We apply our method to biobank data from All of Us and identify quantile-specific patterns of conditional association in a multivariate setting.