ArticleJournal of the American Statistical Association2020
Robust Clustering with Subpopulation-specific Deviations.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A two-step Bayesian clustering approach to relate PFAS mixture profiles and dietary patterns in early pregnancy.Environmental research · 2026Article
- TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS.The annals of applied statistics · 2025Article
- LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Bayesian cluster analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Racial and ethnic heterogeneity in diets of low-income adult females in the United States: results from National Health and Nutrition Examination Surveys from 2011 to 2018.The American journal of clinical nutrition · 2023Observational
- Regularized robust estimation in binary regression models.Journal of applied statistics · 2022Article
- Empirically Derived Dietary Patterns Using Robust Profile Clustering in the Hispanic Community Health Study/Study of Latinos.The Journal of nutrition · 2020Article
- Bayesian estimation methods for survey data with potential applications to health disparities research.Wiley interdisciplinary reviewsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multi-dimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, Robust Profile Clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving pre-pregnancy dietary patterns for women in the NBDPS while accounting for regional variability.
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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.