Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies

TL;DR

This paper introduces a Bayesian latent Gaussian graphical model tailored for verbal autopsies, effectively inferring symptom dependencies from sparse, incomplete data, thereby enhancing cause-of-death predictions and informing questionnaire design.

Contribution

It presents a novel Bayesian approach that incorporates informative priors to improve dependence structure estimation in sparse, high-missingness datasets like verbal autopsies.

Findings

Improved dependence structure estimation with limited data

Enhanced cause-of-death assignment accuracy

Revealed symptom relationships for better questionnaire design

Abstract

Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to accurately learn associations. In this paper, we develop a method for scientific settings where learning dependence structure is essential, but data are sparse and have a high fraction of missing values. Specifically, our work is motivated by survey-based cause of death assessments known as verbal autopsies (VAs). We propose a Bayesian approach to characterize dependence relationships using a latent Gaussian graphical model that incorporates informative priors on the marginal distributions of the variables. We demonstrate such information can improve estimation of the dependence structure, especially in settings with little training data. We show that our method can be integrated into existing probabilistic cause-of-death assignment algorithms and improves model performance while recovering dependence patterns between symptoms that can inform efficient questionnaire design in future data collection.