Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions.

Abstract

Understanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric definition of interaction and facilitates both the discovery and efficient estimation of interaction effects in mixed exposures. Leveraging stochastic shift interventions and ensemble machine learning, InterXshift identifies and quantifies interactions through a model-independent target parameter, estimated using targeted maximum likelihood estimation (TMLE) and cross-validation. The approach contrasts expected outcomes from joint interventions against those from individual exposures, enabling the detection of synergistic and antagonistic interactions. Validation through simulations and application to the National Institute of Environmental Health Sciences (NIEHS) Mixtures Workshop data demonstrate InterXshift's efficacy in accurately identifying true interaction directions and consistently highlighting significant impacts. We apply our methodology to National Health and Nutrition Examination Survey (NHANES) data to understand the interaction effect (if any) of furan exposure on leukocyte telomere length. This method enhances the analysis of multi-exposure interactions within high-dimensional datasets, offering robust methodological improvements for elucidating complex exposure dynamics in environmental health research. Additionally, we provide an opensource implementation of InterXshift in the InterXshift R package, facilitating its adoption and application by the research community.