Bayesian inference for asymptomatic COVID-19 infection rates.
Stat Med
; 41(16): 3131-3148, 2022 07 20.
Article
in English
| MEDLINE | ID: covidwho-1850242
ABSTRACT
To strengthen inferences meta-analyses are commonly used to summarize information from a set of independent studies. In some cases, though, the data may not satisfy the assumptions underlying the meta-analysis. Using three Bayesian methods that have a more general structure than the common meta-analytic ones, we can show the extent and nature of the pooling that is justified statistically. In this article, we reanalyze data from several reviews whose objective is to make inference about the COVID-19 asymptomatic infection rate. When it is unlikely that all of the true effect sizes come from a single source researchers should be cautious about pooling the data from all of the studies. Our findings and methodology are applicable to other COVID-19 outcome variables, and more generally.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Prognostic study
/
Reviews
Limits:
Humans
Language:
English
Journal:
Stat Med
Year:
2022
Document Type:
Article
Affiliation country:
Sim.9408
Similar
MEDLINE
...
LILACS
LIS