Open Access
September 2020 Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies
Zehang Richard Li, Tyler H. McComick, Samuel J. Clark
Bayesian Anal. 15(3): 781-807 (September 2020). DOI: 10.1214/19-BA1172


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.


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Zehang Richard Li. Tyler H. McComick. Samuel J. Clark. "Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies." Bayesian Anal. 15 (3) 781 - 807, September 2020.


Published: September 2020
First available in Project Euclid: 24 September 2019

MathSciNet: MR4132650
Digital Object Identifier: 10.1214/19-BA1172

Keywords: cause of death , high dimensional , mixed data , parameter expansion , spike-and-slab

Vol.15 • No. 3 • September 2020
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