A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes.
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.
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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