Environmental Mixture Methods: Recommendations to Promote Robust Results in the Presence of Random Sampling

Abstract

BACKGROUND AND AIM: Statistical learning is being increasingly used in environmental epidemiology, including in health analyses of environmental mixtures. Statistical learning methods, such as shrinkage methods or kernel smoothing methods, can perform well in instances with complex or high-dimensional data—settings in which traditional statistical methods fail. These novel methods, however, often include random sampling which may induce variability in results. Best practices in data science can help to ensure robustness. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) 2001–2002 cycle to evaluate the potential variability in the estimated association between 18 persistent organic pollutants (POPs) and leukocyte telomere length (LTL) among 1,003 US adults, using four statistical learning models that have been applied previously to analyze the relationship between environmental mixtures and health outcomes. We included two penalized regression methods borrowed from machine learning, lasso and group lasso, and two statistical learning methods designed for environmental health data, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR). We ran each model across 100 initializing values for random number generation (‘seeds’) and assessed variability in resulting estimation and inference. RESULTS:All methods exhibited some seed-dependent variability in results. The degree of variability differed across methods and POPs. Regardless of variability, all methods repeatedly identified furan 2,3,4,7,8-pncdf and PCB 126 as bad actors in the mixture. Both WQS and BKMR consistently estimated a harmful overall mixture effect. CONCLUSIONS:Any statistical learning method reliant on a random seed will exhibit some degree of seed sensitivity. We recommend that researchers repeat their analysis with various seeds as a sensitivity analysis when implementing these methods to enhance interpretability and robustness of results. KEYWORDS: Mixtures, Mixtures analysis, Modeling, Environmental epidemiology, Epidemiology