The Sensitivity of Bayesian Kernel Machine Regression (BKMR) to Data Distribution: A Comprehensive Simulation Analysis

TL;DR

This study investigates how deviations from normal data distributions affect the performance of Bayesian Kernel Machine Regression (BKMR), revealing its sensitivity to data skewness and covariance structures, and emphasizing the need for data assessment before application.

Contribution

It provides a comprehensive simulation analysis showing how data distribution impacts BKMR's robustness, power, and false positive rates, and offers guidance for its proper use in environmental health studies.

Findings

BKMR's power decreases with increased data skewness.

Test sizes become uncontrolled with higher coefficient of variation.

Utilizing covariance information can enhance BKMR's accuracy.

Abstract

Bayesian Kernel Machine Regression (BKMR) has emerged as a powerful tool to detect negative health effects from exposure to complex multi-pollutant mixtures. However, its performance is degraded when data deviate from normality. In this comprehensive simulation analysis, we show that BKMR's power and test size vary under different distributions and covariance matrix structures. Our results demonstrate specifically that BKMR's robustness is influenced by the response's coefficient of variation (CV), resulting in reduced accuracy to detect true effects when data are skewed. Test sizes become uncontrolled (> 0.05) as CV values increase, leading to inflated false detection rates. However, we find that BKMR effectively utilizes off-diagonal covariance information corresponding to predictor interdependencies, increasing statistical power and accuracy. To achieve reliable and accurate results, we advocate for scrutiny of data skewness and covariance before applying BKMR, particularly when used to predict cognitive decline from blood/urine heavy metal concentrations in environmental health contexts.