23: Evaluating Exposure Mixture Analysis Techniques for Pooling Multiple Cohorts

Music City Center 

Environmental exposures during critical developmental periods significantly impact children's health, yet analyzing complex exposure mixtures across diverse populations remains challenging. We propose a unified framework combining mixture analysis and machine learning techniques for pooling multiple cohort data. Through comprehensive simulation studies, we evaluate methods including Weighted Quantile Sum (WQS), Bayesian Kernel Machine Regression (BKMR), quantile-based g-computation, and Partial Linear Single Index Model (PLSIM), assessing their performance across various scenarios of cohort heterogeneity, exposure correlations, and missing data patterns. We apply this framework to examine prenatal air pollution exposure effects on autism traits using data from over eight thousand mother-child pairs across multiple cohorts from the Environmental influences on Child Health Outcomes (ECHO) program. Our approach integrates ensemble learning, meta-learning algorithms, and Bayesian hierarchical models to account for between-cohort heterogeneity while maximizing information sharing. This methodology promises to enhance our understanding of environmental exposure effects across diverse pop

Environmental mixture analysis

Machine learning

Heterogeneity

ECHO program 

Section on Statistics in Epidemiology