Seminar by Dr. Zhenke WU from Department of Biostatistics, University of Michigan

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. This is joint work with PhD student Mengbing Li (UMich Biostat) and Professor Briana Stephenson (Harvard Biostat).

Dr. Wu is an Associate Professor in the Department of Biostatistics at the University of Michigan. He obtained his PhD from Johns Hopkins University in 2014. His research is motivated by biomedical and public health problems and is centred on the design and application of statistical methods that inform health decisions made by individuals, or precision medicine. Towards this goal, he focuses on two lines of methodological research: a) structured Bayesian latent variable models for clustering and disease subtyping, and b) study design and causal methods for evaluating sequential interventions that tailor to individuals' changing circumstances such as in mobile health studies.