BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS

• 成果类型: Article
• 署名作者: Li, Zehang Richard; Wu, Zhenke; Chen, Irena; Clark, Samuel J.
• 署名单位: University of California System; University of California Santa Cruz; University of Michigan System; University of Michigan; Max Planck Society; University System of Ohio; Ohio State University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1826
• 发表日期: 2024
• 页码: 1137-1159
• 关键词: probabilistic cause; adaptation; inference

摘要

Understanding cause-specific mortality rates is crucial for monitoring population health and designing public health interventions. Worldwide, twothirds of deaths do not have a cause assigned. Verbal autopsy (VA) is a wellestablished tool to collect information describing deaths outside of hospitals by conducting surveys to caregivers of a deceased person. It is routinely implemented in many low- and middle-income countries. Statistical algorithms to assign cause of death using VAs are typically vulnerable to the distribution shift between the data used to train the model and the target population. This presents a major challenge for analyzing VAs, as labeled data are usually unavailable in the target population. This article proposes a latent class model framework for VA data (LCVA) that jointly models VAs collected over multiple heterogeneous domains, assigns causes of death for out-of-domain observations and estimates cause-specific mortality fractions for a new domain. We introduce a parsimonious representation of the joint distribution of the collected symptoms using nested latent class models and develop a computationally efficient algorithm for posterior inference. We demonstrate that LCVA outperforms existing methods in predictive performance and scalability. Supplementary Material and reproducible analysis codes are available online. The R package LCVA implementing the method is available on GitHub (https://github.com/richardli/LCVA).