  Dietary patterns synthesize multiple related diet components, which can be
used by nutrition researchers to examine diet-disease relationships. Latent
class models (LCMs) have been used to derive dietary patterns from dietary
intake assessment, where each class profile represents the probabilities of
exposure to a set of diet components. However, LCM-derived dietary patterns can
exhibit strong similarities, or weak separation, resulting in numerical and
inferential instabilities that challenge scientific interpretation. This issue
is exacerbated in small-sized subpopulations. To address these issues, we
provide a simple solution that empowers LCMs to improve dietary pattern
estimation. We develop a tree-regularized Bayesian LCM that shares statistical
strength between dietary patterns to make better estimates using limited data.
This is achieved via a Dirichlet diffusion tree process that specifies a prior
distribution for the unknown tree over classes. Dietary patterns that share
proximity to one another in the tree are shrunk towards ancestral dietary
patterns a priori, with the degree of shrinkage varying across pre-specified
food groups. Using dietary intake data from the Hispanic Community Health
Study/Study of Latinos, we apply the proposed approach to a sample of 496 US
adults of South American ethnic background to identify and compare dietary
patterns.
