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A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes.
Gao, Xiang; Dong, Qunfeng.
  • Gao X; Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois 60153, USA.
  • Dong Q; Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois 60153, USA.
JAMIA Open ; 3(4): 628-631, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1096540
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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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: JAMIA Open Year: 2020 Document Type: Article Affiliation country: Jamiaopen

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: JAMIA Open Year: 2020 Document Type: Article Affiliation country: Jamiaopen