A Hierarchical Integrative Group LASSO (HiGLASSO) Framework for Analyzing Environmental Mixtures
Jonathan Boss, Alexander Rix, Yin-Hsiu Chen, Naveen N. Narisetty,, Zhenke Wu, Kelly K. Ferguson, Thomas F. McElrath, John D. Meeker, Bhramar, Mukherjee

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…
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Taxonomy
TopicsAir Quality and Health Impacts · Toxic Organic Pollutants Impact · Health, Environment, Cognitive Aging
