Discovery of Critical Thresholds in Mixed Exposures and Estimation of Policy Intervention Effects using Targeted Learning

TL;DR

This paper introduces a novel algorithm and framework for identifying critical exposure thresholds in multiple chemical exposures and estimating the effects of policy interventions, addressing overfitting issues in data analysis.

Contribution

The authors develop a data-adaptive method for discovering exposure thresholds and estimating policy effects simultaneously, with proven convergence and application to real data.

Findings

Algorithm effectively finds exposure thresholds maximizing or minimizing outcomes.

Simulation studies confirm asymptotic convergence to true effects.

Application to NHANES data identifies harmful metal exposures affecting telomere length.

Abstract

Traditional regulations of chemical exposure tend to focus on single exposures, overlooking the potential amplified toxicity due to multiple concurrent exposures. We are interested in understanding the average outcome if exposures were limited to fall under a multivariate threshold. Because threshold levels are often unknown a priori, we provide an algorithm that finds exposure threshold levels where the expected outcome is maximized or minimized. Because both identifying thresholds and estimating policy effects on the same data would lead to overfitting bias, we also provide a data-adaptive estimation framework, which allows for both threshold discovery and policy estimation. Simulation studies show asymptotic convergence to the optimal exposure region and to the true effect of an intervention. We demonstrate how our method identifies true interactions in a public synthetic mixture data set. Finally, we applied our method to NHANES data to discover metal exposures that have the most harmful effects on telomere length. We provide an implementation in the CVtreeMLE R package.