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Poor diet quality is a key modifiable risk factor for hypertension anddisproportionately impacts low-income women. Analyzing diet-driven hypertensiveoutcomes in this demographic can be challenging due to scarcity of data, aswell as high-dimensionality, multi-collinearity, and selection bias in thesampled exposures. Supervised Bayesian model-based clustering methods allowdietary data to be summarized into latent patterns that holistically capturecomplex relationships among foods and a known health outcome but do notsufficiently account for complex survey design. This leads to biased estimationand inference. To address this issue, we propose a supervised weightedoverfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihoodapproach that can integrate sampling weights into an exposure-outcome model fordiscrete data. Our model adjusts for stratification, clustering, andinformative sampling, and handles modifying effects via interaction termswithin a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studiesconfirm that the SWOLCA model exhibits good performance in terms of bias,precision, and coverage. Using data collected from the National Health andNutrition Examination Survey (2015-2018), we demonstrate the utility of ourmodel by characterizing dietary patterns associated with hypertensive outcomesamong low-income women in the United States.

Derivation of outcome-dependent dietary patterns for low-income women  obtained from survey data using a Supervised Weighted Overfitted Latent Class  Analysis