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Supervised Robust Profile Clustering

In many studies, dimension reduction methods are used to profile participant characteristics. For example, nutrition epidemiologists often use latent class models to characterize dietary patterns. One challenge with such approaches is understanding subtle variations in patterns across subpopulations. Robust Profile Clustering (RPC) provides a dual flexible clustering model, where participants may cluster at two levels: (1) globally, where participants are clustered according to behaviors shared across an overall population, and (2) locally, where individual behaviors can deviate and cluster in subpopulations. We link clusters to a health outcome using a joint model. This model is used to derive dietary patterns in the United States and evaluate case proportion of orofacial clefts. Using dietary consumption data from the 1997-2009 National Birth Defects Prevention Study, a population-based case-control study, we determine how maternal dietary profiles are associated with an orofacial cleft among offspring. Results indicated that mothers who consumed a high proportion of fruits and vegetables compared to meats, such as chicken and beef, had lower odds delivering a child with an orofacial cleft defect.