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Dietary patterns synthesize multiple related diet components, which can beused by nutrition researchers to examine diet-disease relationships. Latentclass models (LCMs) have been used to derive dietary patterns from dietaryintake assessment, where each class profile represents the probabilities ofexposure to a set of diet components. However, LCM-derived dietary patterns canexhibit strong similarities, or weak separation, resulting in numerical andinferential instabilities that challenge scientific interpretation. This issueis exacerbated in small-sized subpopulations. To address these issues, weprovide a simple solution that empowers LCMs to improve dietary patternestimation. We develop a tree-regularized Bayesian LCM that shares statisticalstrength between dietary patterns to make better estimates using limited data.This is achieved via a Dirichlet diffusion tree process that specifies a priordistribution for the unknown tree over classes. Dietary patterns that shareproximity to one another in the tree are shrunk towards ancestral dietarypatterns a priori, with the degree of shrinkage varying across pre-specifiedfood groups. Using dietary intake data from the Hispanic Community HealthStudy/Study of Latinos, we apply the proposed approach to a sample of 496 USadults of South American ethnic background to identify and compare dietarypatterns.

Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulations