A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures

TL;DR

This paper introduces HiGLASSO, a hierarchical group LASSO framework that effectively identifies nonlinear and interaction effects of environmental pollutants on health outcomes, addressing computational and modeling challenges.

Contribution

The paper presents a novel hierarchical integrative group LASSO method that enforces strong heredity, incorporates adaptive weights, and induces sparsity for analyzing environmental mixtures.

Findings

Successfully applied to birth cohort data revealing joint effects of toxicants

Proves sparsistency of the proposed method

Provides an accessible R package for implementation

Abstract

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group LASSO (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.