ArticleJournal of the American Statistical Association2023
A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.
Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion).Bayesian analysis · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
- Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models.Journal of the American Statistical Association · 2025Article
- Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data.Journal of the American Statistical Association · 2025Article
- Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.Biometrics · 2024Article
- Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.Journal of machine learning research : JMLR · 2024Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials.Journal of the American Statistical Association · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.