Quantitative Issues in Cancer Research Working Group Seminar
Daniel Schwartz, Postdoctoral Research Fellow, Department of Biostatistics, Harvard University
Dynamic Latent Factor Models To Infer Dietary Patterns From Nutrition Survey Data
Abstract: 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 provide limited insights because they use simplistic measures of diet measured at a single timepoint. To address this issue, we develop a Bayesian dynamic latent factor model to succinctly describe multivariate dietary patterns. Our approach flexibly incorporates multivariate, longitudinal nutrition survey data such as food frequency questionnaires with multiple outcome types (e.g. ordinal, continuous, etc.). A truncated 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 of interest. As a motivating application we consider 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.