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Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data

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  • Kelly R. Moran
  • Elizabeth L. Turner
  • David Dunson
  • Amy H. Herring

Abstract

In low‐resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause‐specific mortality fraction (CSMF) for populations. Post‐mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense and/or cultural norms. For this reason, verbal autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness‐of‐fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.

Suggested Citation

  • Kelly R. Moran & Elizabeth L. Turner & David Dunson & Amy H. Herring, 2021. "Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 532-557, June.
  • Handle: RePEc:bla:jorssc:v:70:y:2021:i:3:p:532-557
    DOI: 10.1111/rssc.12468
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    References listed on IDEAS

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    1. Antonio Canale & David B. Dunson, 2013. "Nonparametric Bayes modelling of count processes," Biometrika, Biometrika Trust, vol. 100(4), pages 801-816.
    2. Durante, Daniele, 2017. "A note on the multiplicative gamma process," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 198-204.
    3. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, vol. 98(2), pages 291-306.
    4. Tsuyoshi Kunihama & Zehang Richard Li & Samuel J. Clark & Tyler H. McCormick, 2018. "Bayesian factor models for probabilistic cause of death assessment with verbal autopsies," Discussion Paper Series 177, School of Economics, Kwansei Gakuin University, revised Mar 2018.
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