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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), 521-37. 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

Journal Title

Journal of the American Statistical Association

Author(s)

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

Year Published

2020

Volume Number

115

Issue Number

530

Pages

521-37

PMCID

PMC7500490

Reference ID

13232