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Evaluating Exposure Mixture Analysis Techniques for Pooling Multiple Cohorts

Abstract Number:

1816 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Yuyan Wang (1), Akhgar Ghassabian (2), Mengling Liu (3), Ruojin Song (4)

Institutions:

(1) New York University, N/A, (2) NYU Langone Health, N/A, (3) New York University Grossman School of Medicine, N/A, (4) New York University, NY

Co-Author(s):

Akhgar Ghassabian  
NYU Langone Health
Mengling Liu  
New York University Grossman School of Medicine
Ruojin Song  
New York University

First Author:

Yuyan Wang  
New York University

Presenting Author:

Yuyan Wang  
New York University

Abstract Text:

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

Keywords:

Environmental mixture analysis|Machine learning|Heterogeneity|ECHO program| |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Statistical Issues in Environmental Epidemiology

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