A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes.
JAMIA Open 2020;
3:628-631. [PMID:
33619468 PMCID:
PMC7750711 DOI:
10.1093/jamiaopen/ooaa062]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 10/29/2020] [Indexed: 12/01/2022] Open
Abstract
A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.
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