Bayesian Analysis

Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies

Zehang Richard Li, Tyler H. McComick, and Samuel J. Clark

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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.

Article information

Source
Bayesian Anal., Advance publication (2018), 27 pages.

Dates
First available in Project Euclid: 24 September 2019

Permanent link to this document
https://projecteuclid.org/euclid.ba/1569290444

Digital Object Identifier
doi:10.1214/19-BA1172

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

Li, Zehang Richard; McComick, Tyler H.; Clark, Samuel J. Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies. Bayesian Anal., advance publication, 24 September 2019. doi:10.1214/19-BA1172. https://projecteuclid.org/euclid.ba/1569290444


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Supplemental materials

  • Supplementary Material to “Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies”. PDF document of supplementary material. The replication R and Java codes to implement the proposed method can be found in the repository at https://github.com/richardli/LGGM.
    Digital Object Identifier: doi:10.1214/19-BA1172SUPP
    Supplemental PDF (2331 KB)

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