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Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies. (English) Zbl 1459.62096
Summary: 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.
Reviewer: Reviewer (Berlin)
MSC:
62H22 Probabilistic graphical models
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62N05 Reliability and life testing
62P10 Applications of statistics to biology and medical sciences; meta analysis
92D25 Population dynamics (general)
Software:
BDgraph; bfa; EMVS; GHS; HdBCS; openVA; R
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References:
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