Two-step approach for assessing the health effects of environmental chemical mixtures: application to simulated datasets and real data from the Navajo Birth Cohort Study

Abstract

Background: There 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. Methods: We 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. Results: Utilizing 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. Conclusions: Our 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.

Profile of patients undergoing speech-language therapy in a physical and rehabilitation medicine hospital service

“…This must be interpreted in the context of the health device studied since the public system and the National Health Fund (FONASA) cover the population with the lowest income in Chile 34 . Regarding the seasonality observed in the requirement for speech therapy in the cohort, the literature indicates that there are environmental determinants that may influence the behavior of patients seeking medical or rehabilitation services 35,36 due to respiratory exacerbations, heart attacks, or cerebrovascular disease, among others [37][38][39] . However, there is no evidence related to the association between these factors and the provision of care by speech therapists, which is considered a precedent for future research.…”

Prescription Opioid Laws and Opioid Dispensing in US Counties

“…Other studies have used random forests for dimension reduction and LASSO for subsequent modeling. 72 In contrast, we used LASSO models for preprocessing, as variable selection in random forest models may be unstable when low-relevance or correlated variables are included, and because we were interested in identifying provision combinations—a task better suited to random forest rather than LASSO approaches. Finally, because our analyses included all provisions simultaneously—so that we could systematically compare provisions in terms of predictive relevance—we could not incorporate time lags beyond the 6-month sensitivity analysis.…”

Variable Selection in High Dimensional Data with Interactions

“…For the purpose of applying dimension reduction and determining the strength of an association between a variable that includes interaction terms, Li L. [10] presented a double step method consolidating Random forest and ALasso. They concluded that the proposed method outperformed the other techniques by producing a parsimonious model.…”