Abstract Number:
3844
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Daniel Schwartz (1), Briana Stephenson (1), Xihong Lin (1)
Institutions:
(1) Harvard T.H. Chan School of Public Health, N/A
Co-Author(s):
Xihong Lin
Harvard T.H. Chan School of Public Health
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
nutrition|Bayesian inference|latent factor model|health disparities|survey data|
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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