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Robust Clustering with Subpopulation-Specific Deviations

Citation

Stephenson, Briana J. K.; Herring, Amy H.; & Olshan, Andrew F. (2020). Robust Clustering with Subpopulation-Specific Deviations. Journal of the American Statistical Association, 115(530), 521-537. PMCID: PMC7500490

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.

URL

http://dx.doi.org/10.1080/01621459.2019.1611583

Reference Type

Journal Article

Year Published

2020

Journal Title

Journal of the American Statistical Association

Author(s)

Stephenson, Briana J. K.
Herring, Amy H.
Olshan, Andrew F.

PMCID

PMC7500490