Dynamic Latent Factor Models To Infer Dietary Patterns From Longitudinal Nutrition Survey Data

Oregon Convention Center 

A growing body of research has shown that poor diet is a leading risk factor for death, especially in connection with chronic diseases such as cardiovascular disease. However, these studies are limited because they use simplistic measures of diet measured at a single timepoint. To address this issue, we develop a Bayesian dynamic latent factor model that measures how multivariate dietary patterns change over time. Our approach flexibly incorporates multivariate, longitudinal nutrition survey data such as food frequency questionaires with multiple outcome types (e.g. ordinal, continuous, etc.). A multiplicative gamma process prior is placed on the factor loadings to adaptively estimate low-dimensional dietary patterns. Importantly, our model also incorporates covariates such as demographics to assess how dietary patterns differ across subpopulations. We evaluate the Frequentist operating characteristics of the method in a simulation study. Our motivating application is the Black Women's Health Study, where we construct dynamic measures of diet that will be used in downstream analyses to better understand cardiovascular disease risk among black women in the United States.

nutrition

Bayesian inference

latent factor model

health disparities

survey data 

Biometrics Section