Neural Network-based Partial-Linear Single-Index Models for Environmental Mixtures Analysis

TL;DR

This paper introduces NeuralPLSI, a neural network-based framework that combines interpretability and flexibility for analyzing complex environmental mixtures and their health effects across diverse outcomes.

Contribution

It proposes a novel neural network-based partial-linear single-index model that enhances interpretability, scalability, and applicability in environmental mixture analysis.

Findings

Demonstrates effectiveness through simulation studies.

Shows practical utility with NHANES data analysis.

Provides an open-source software package for implementation.

Abstract

Evaluating the health effects of complex environmental mixtures remains a central challenge in environmental health research. Existing approaches vary in their flexibility, interpretability, scalability, and support for diverse outcome types, often limiting their utility in real-world applications. To address these limitations, we propose a neural network-based partial-linear single-index (NeuralPLSI) modeling framework that bridges semiparametric regression modeling interpretability with the expressive power of deep learning. The NeuralPLSI model constructs an interpretable exposure index via a learnable projection and models its relationship with the outcome through a flexible neural network. The framework accommodates continuous, binary, and time-to-event outcomes, and supports inference through a bootstrap-based procedure that yields confidence intervals for key model parameters. We evaluated NeuralPLSI through simulation studies under a range of scenarios and applied it to data from the National Health and Nutrition Examination Survey (NHANES) to demonstrate its practical utility. Together, our contributions establish NeuralPLSI as a scalable, interpretable, and versatile modeling tool for mixture analysis. To promote adoption and reproducibility, we release a user-friendly open-source software package that implements the proposed methodology and supports downstream visualization and inference (\texttt{https://github.com/hyungrok-do/NeuralPLSI}).