Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a Supervised Weighted Overfitted Latent Class Analysis
arxiv(2023)
摘要
Poor diet quality is a key modifiable risk factor for hypertension and
disproportionately impacts low-income women. Analyzing diet-driven
hypertensive outcomes in this demographic is challenging due to the complexity
of dietary data and selection bias when the data come from surveys, a main data
source for understanding diet-disease relationships in understudied
populations. Supervised Bayesian model-based clustering methods summarize
dietary data into latent patterns that holistically capture relationships among
foods and a known health outcome but do not sufficiently account for complex
survey design. This leads to biased estimation and inference and lack of
generalizability of the patterns. To address this, we propose a supervised
weighted overfitted latent class analysis (SWOLCA) based on a Bayesian
pseudo-likelihood approach that integrates sampling weights into an
exposure-outcome model for discrete data. Our model adjusts for stratification,
clustering, and informative sampling, and handles modifying effects via
interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm.
Simulation studies confirm that the SWOLCA model exhibits good performance in
terms of bias, precision, and coverage. Using data from the National Health and
Nutrition Examination Survey (2015-2018), we demonstrate the utility of our
model by characterizing dietary patterns associated with hypertensive outcomes
among low-income women in the United States.
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