Abstract
BackgroundThere is increasing interest in examining the consequences of simultaneous exposures to chemical mixtures. However, a consensus or recommendations on how to appropriately select the statistical approach analyzing the health effects of mixture exposures which best aligns with study goals has not been well established. We recognize the limitations that existing methods have in effectively reducing data dimension and detecting interaction effects when analyzing chemical mixture exposures collected in high dimensional datasets with varying degrees of variable intercorrelations. In this research, we aim to examine the performance of a two-step statistical approach in addressing the analytical challenges of chemical mixture exposures using two simulated data sets, and an existing data set from the Navajo Birth Cohort Study as a representative case study.MethodsWe propose to use a two-step approach: a robust variable selection step using the random forest approach followed by adaptive lasso methods that incorporate both dimensionality reduction and quantification of the degree of association between the chemical exposures and the outcome of interest, including interaction terms. We compared the proposed method with other approaches including (1) single step adaptive lasso; and (2) two-step Classification and regression trees (CART) followed by adaptive lasso method.ResultsUtilizing simulated data sets and applying the method to a real-life dataset from the Navajo Birth Cohort Study, we have demonstrated good performance of the proposed two-step approach. Results from the simulation datasets indicated the effectiveness of variable dimension reduction and reliable identification of a parsimonious model compared to other methods: single-step adaptive lasso or two-step CART followed by adaptive lasso method.ConclusionsOur proposed two-step approach provides a robust way of analyzing the effects of high-throughput chemical mixture exposures on health outcomes by combining the strengths of variable selection and adaptive shrinkage strategies.
Highlights
There is increasing interest in examining the consequences of simultaneous exposures to chemical mixtures
To accommodate the analytical challenges in evaluating the health effects of the complex multiple interacting chemical mixture exposures, we propose to use a two-step approach: a robust variable selection step using the random forest approach followed by adaptive lasso methods
The random forest (RF) followed by adaptive lasso method identified X1, X5 and X7 to be significantly associated with the outcome, where the coefficient estimates are similar to those identified using the single step adaptive lasso approach
Summary
There is increasing interest in examining the consequences of simultaneous exposures to chemical mixtures. A consensus or recommendations on how to appropriately select the statistical approach analyzing the health effects of mixture exposures which best aligns with study goals has not been well established. To accommodate the analytical challenges in evaluating the health effects of the complex multiple interacting chemical mixture exposures, we propose to use a two-step approach: a robust variable selection step using the random forest approach followed by adaptive lasso methods. We expect this two-step approach incorporates both dimensionality reduction and quantification of the degree of association between the chemical exposures and the outcome of interest, including interaction terms. Considering the proximity of Navajo community members to AUMs, it is of importance to systematically investigate the health impact of simultaneous exposures to multiple metals
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