ArticleThe annals of applied statistics2020
BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES.
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Hierarchical latent class models for mortality surveillance using partially verified verbal autopsies.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data.Statistics in medicine · 2026Article
- Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.Biostatistics (Oxford, England) · 2024Article
- BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS.The annals of applied statistics · 2024Article
- Comparison of causes of stillbirth and child deaths as determined by verbal autopsy and minimally invasive tissue sampling.PLOS global public health · 2024Article
- The openVA Toolkit for Verbal Autopsies.The R journal · 2022Article
- Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies.Bayesian analysis · 2020Article
- BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES.The annals of applied statistics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data.
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Registered trials
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