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• W4280631308 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." @default.
• W4280631308 created "2022-05-22" @default.
• W4280631308 creator A5045894674 @default.
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• W4280631308 date "2022-05-18" @default.
• W4280631308 modified "2023-09-24" @default.
• W4280631308 title "Bayesian inference for asymptomatic COVID‐19 infection rates" @default.
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• W4280631308 doi "https://doi.org/10.1002/sim.9408" @default.
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