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Comparison of Methods for Analyzing Environmental Mixtures Effects on Survival Outcomes
Melanie N Mayer1, Arce Domingo-Relloso2, Marianthi-Anna Kioumourtzoglou3,4,5
1Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Melanie.Mayer@PennMedicine.upenn.edu.
Current Environmental Health Reports
|November 1, 2025
Summary
Advanced statistical models for environmental mixtures show promise but face challenges. Flexible models improve survival outcome estimation but can be variable, requiring careful evaluation in real-world epidemiological studies.
Area of Science:
- Environmental Epidemiology
- Biostatistics
- Toxicology
Background:
- Estimating environmental mixture effects on survival is crucial in epidemiology.
- Advanced modeling techniques are increasingly used, but their performance needs evaluation.
Purpose of the Study:
- To identify and evaluate advanced statistical methods for analyzing environmental mixtures and survival outcomes.
- To assess the applicability and performance of these methods through simulations.
Main Methods:
- Comparison of five statistical methods: Cox Proportional Hazards (with/without penalized splines), Cox Elastic Net, Bayesian Additive Regression Trees (BART), and Multivariate Adaptive Regression Splines (MARS).
- Performance evaluation via simulations under various exposure correlation and proportional hazards scenarios.
Main Results:
- Log-linear models showed poor coverage for individual and mixture effects, especially with correlated exposures or violated assumptions.
- Flexible models (BART, MARS) offered better coverage but exhibited higher variability and bias.
- Challenges remain for applying flexible models to environmental mixtures in survival analysis due to sample size and censoring.
Conclusions:
- Flexible statistical models can improve estimation of environmental mixture effects on survival outcomes compared to constrained models.
- High variability and potential bias necessitate careful application and validation of flexible models.
- Researchers should consider evaluating findings across multiple methods due to inherent complexities.
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