When are novel methods for analyzing complex chemical mixtures in epidemiology beneficial?

TL;DR

This study compares general and novel statistical methods for analyzing complex chemical mixtures in epidemiology, finding that the choice depends on correlation, interactions, and effect directions among mixture components.

Contribution

It provides a comprehensive empirical comparison of methods, guiding researchers on when to use general versus novel approaches for chemical mixture analysis.

Findings

General methods perform well with moderate correlation and simple effects.

Novel methods excel with high interactions or correlated exposures.

The paper offers guidelines for method selection based on data characteristics.

Abstract

Estimating the health impacts of exposure to a mixture of chemicals poses many statistical challenges: multiple correlated exposure variables, moderate to high dimensionality, and possible nonlinear and interactive health effects of mixture components. Reviews of chemical mixture methods aim to help researchers select a statistical method suited to their goals and data, but examinations of empirical performance have emphasized novel methods purpose-built for analyzing complex chemical mixtures, or other more advanced methods, over more general methods which are widely used in many application domains. We conducted a broad experimental comparison, across simulated scenarios, of both more general methods (such as generalized linear models) and novel methods (such as Bayesian Kernel Machine Regression) designed to study chemical mixtures. We assessed methods based on their ability to control Type I error rate, maximize power, provide interpretable results, and make accurate predictions. We find that when there is moderate correlation between mixture components and the exposure-response function does not have complicated interactions, or when mixture components have opposite effects, general methods are preferred over novel ones. With highly interactive exposure-response functions or highly correlated exposures, novel methods provide important benefits. We provide a comprehensive summary of when different methods are most suitable.