Overcoming Statistical challenges in the analysis of dietary intake changes over time

Music City Center 

The analysis of repeated measures of dietary intake data is often met with statistical challenges due to its high dimensionality and heterogeneity. These issues are further amplified when data is obtained from large population cohort studies. Bayesian nonparametric model-based clustering offers the computational flexibility to handle multiple exposures jointly, as well as the mechanics to identify subgroup differences and similarities through the borrowing of information across subgroups. This talk will discuss approaches that can accommodate a wide set of dietary exposure variables that interrelate and change over time, as well as the scalability of this data for large, heterogeneous populations. Using dietary intake data from over 58.000 women collected from the Black Women's Health Study, we will apply these approaches to better understand dietary consumption patterns in US amongst Black Women from 1995-2021.

diet patterns

Bayesian nonparametric

model-based clustering