Robust Clustering With Subpopulation-Specific Deviations

• 成果类型: Article
• 署名作者: Stephenson, Briana J. K.; Herring, Amy H.; Olshan, Andrew
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Duke University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1611583
• 发表日期: 2020
• 页码: 521-537
• 关键词: latent class analysis; dietary patterns; mixture-models; number; RISK

摘要

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 multidimensional, 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 prepregnancy dietary patterns for women in the NBDPS while accounting for regional variability. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.