LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES.
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2025-03
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Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.
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Palmer, Glenn, Amy H Herring and David B Dunson (2025). LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES. The annals of applied statistics, 19(1). pp. 769–797. 10.1214/24-aoas1988 Retrieved from https://hdl.handle.net/10161/32451.
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Amy H Herring
Interests include Bayesian methods, complex data structures (correlated data, multivariate data recorded over time, data on different measurement scales), dimension reduction methods, and applications in health and medicine.
Recently I was honored to present work in progress on discovery of sepsis subtypes in sub-Saharan Africa as the University of Cambridge's 20th Armitage Lecturer.
Other projects include investigation of health effects of exposures to mixtures of environmental contaminants (particularly in relation to child health and neurodevelopment), theory and methods addressing robustness of clustering methods, and development of methods in reproductive and environmental epidemiology.
Duke today provided a nice overview of some of my work.
Team News:
- Congratulations to Glenn Palmer whose paper on low rank longitudinal factor regression was just accepted by AOAS!
- Congratulations to Alex Dombowsky for winning a best talk award at BAYSM:O in November 2023!
- Congratulations to Phuc Nguyen who successfully defended her dissertation and has moved to San Francisco to join LinkedIn!
- Congratulations to Bora Jin who graduated with her PhD and has moved to Johns Hopkins University for a postdoc with Abhi Datta!
- Congratulations to Maoran Xu who won an ICSA poster award for our work with David Dunson on NIFTY (coming soon to the arXiv)
- Congratulations to Alex Dombowsky for winning the Myra and William Waldo Boone Fellowship at Duke!
David B. Dunson
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties of methods we develop.
Some highlight application areas:
(1) Modeling of biological communities and biodiversity - we are considering global data on fungi, insects, birds and animals including DNA sequences, images, audio, etc. Data contain large numbers of species unknown to science and we would like to learn about these new species, community network structure, and the impact of environmental change and climate.
(2) Brain connectomics - based on high resolution imaging data of the human brain, we are seeking to developing new statistical and machine learning models for relating brain networks to human traits and diseases.
(3) Environmental health & mixtures - we are building tools for relating chemical and other exposures (air pollution etc) to human health outcomes, accounting for spatial dependence in both exposures and disease. This includes an emphasis on infectious disease modeling, such as COVID-19.
Some statistical areas that play a prominent role in our methods development include models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes, other stochastic processes), Bayesian inference frameworks, efficient sampling and analytic approximation algorithms, and models for "object data" (trees, networks, images, spatial processes, etc).
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