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

• URL: http://arxiv.org/abs/2512.11593v1
• Date: Fri, 12 Dec 2025 14:28:47 GMT
• Title: Neural Network-based Partial-Linear Single-Index Models for Environmental Mixtures Analysis
• Authors: Hyungrok Do, Yuyan Wang, Mengling Liu, Myeonggyun Lee,
• Abstract summary: We propose a neural network-based partial-linear single-index (NeuralPLSI) modeling framework.<n>NeuralPLSI bridges semiparametric regression modeling interpretability with the expressive power of deep learning.<n>Our contributions establish NeuralPLSI as a scalable, interpretable, and versatile modeling tool for mixture analysis.
• Score: 10.274230637691717
• License: http://creativecommons.org/licenses/by/4.0/
• 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}).

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