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Activity Number: 484 - Applied Bayesian Methodology
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #322548
Title: Bayesian Inference for Asymptomatic COVID-19 Infection Rates
Author(s): Dexter Cahoy* and Joseph Sedransk
Companies: University of Houston-Downtown and University of Maryland
Keywords: Dirichlet process mixture; Exchangeable random variables; Meta-analysis; Pooling results; Reversible jump Markov Chain Monte Carlo; SARS-CoV-2
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. We investigate by re-analyzing 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.


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