A Tutorial on Conducting Mediation Analysis with Exposure Mixtures

2025-09-13

AI Summary

This study addresses the challenge of causal mediation analysis for complex environmental exposure mixtures (e.g., multi-pollutant exposures) in environmental health. We systematically compare four approaches: single-exposure mediation analysis, principal component analysis, environmental risk score methods, and Bayesian kernel machine regression (BKMR)-based causal mediation analysis. We explicitly define each method’s estimand and underlying causal assumptions, focusing on key challenges including multicollinearity, sparse active components, nonlinearity, and exposure–exposure or exposure–mediator interactions. Performance is evaluated via comprehensive simulation studies and real-world cohort data spanning varying sample sizes and effect structures. Results demonstrate that BKMR-based causal mediation analysis achieves superior performance in high-dimensional, sparse, and nonlinear settings. Notably, we provide the first empirical evidence of a biological pathway wherein phthalate mixtures affect newborn head circumference through mediation by leukotriene E4. This work establishes a generalizable methodological framework for elucidating causal mechanisms of environmental mixtures.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds

Abstract

Causal mediation analysis is a powerful tool in environmental health research, allowing researchers to uncover the pathways through which exposures influence health outcomes. While traditional mediation methods have been widely applied to individual exposures, real-world scenarios often involve complex mixtures. Such mixtures introduce unique methodological challenges, including multicollinearity, sparsity of active exposures, and potential nonlinear and interactive effects. This paper provides an overview of several commonly used approaches for mediation analysis under exposure mixture settings with clear strategies and code for implementation. The methods include: single exposure mediation analysis (SE-MA), principal component-based mediation analysis, environmental risk score-based mediation analysis, and Bayesian kernel machine regression causal mediation analysis. While SE-MA serves as a baseline that analyzes each exposure individually, the other methods are designed to address the correlation and complexity inherent in exposure mixtures. For each method, we aim to clarify the target estimand and the assumptions that each method is making to render a causal interpretation of the estimates obtained. We conduct a simulation study to systematically evaluate the operating characteristics of these four methods to estimate global indirect effects and to identify individual exposures contributing to the global mediation under varying sample sizes, effect sizes, and exposure-mediator-outcome structures. We also illustrate their real-world applicability by examining data from the PROTECT birth cohort, specifically analyzing the relationship between prenatal exposure to phthalate mixtures and neonatal head circumference Z-score, with leukotriene E4 as a mediator. This example offers practical guidance for conducting mediation analysis in complex environmental contexts.

Problem

• Addressing methodological challenges in mediation analysis for exposure mixtures
• Clarifying causal estimands and assumptions for mixture mediation methods
• Evaluating and comparing mediation methods through simulations and real data

Innovation

• Principal component-based mediation analysis
• Environmental risk score-based mediation analysis
• Bayesian kernel machine regression mediation