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• W3147657356 startingPage "3492" @default.
• W3147657356 abstract "Bayesian methods are an important set of tools for performing meta-analyses. They avoid some potentially unrealistic assumptions that are required by conventional frequentist methods. More importantly, meta-analysts can incorporate prior information from many sources, including experts’ opinions and prior meta-analyses. Nevertheless, Bayesian methods are used less frequently than conventional frequentist methods, primarily because of the need for nontrivial statistical coding, while frequentist approaches can be implemented via many user-friendly software packages. This article aims at providing a practical review of implementations for Bayesian meta-analyses with various prior distributions. We present Bayesian methods for meta-analyses with the focus on odds ratio for binary outcomes. We summarize various commonly used prior distribution choices for the between-studies heterogeneity variance, a critical parameter in meta-analyses. They include the inverse-gamma, uniform, and half-normal distributions, as well as evidence-based informative log-normal priors. Five real-world examples are presented to illustrate their performance. We provide all of the statistical code for future use by practitioners. Under certain circumstances, Bayesian methods can produce markedly different results from those by frequentist methods, including a change in decision on statistical significance. When data information is limited, the choice of priors may have a large impact on meta-analytic results, in which case sensitivity analyses are recommended. Moreover, the algorithm for implementing Bayesian analyses may not converge for extremely sparse data; caution is needed in interpreting respective results. As such, convergence should be routinely examined. When select statistical assumptions that are made by conventional frequentist methods are violated, Bayesian methods provide a reliable alternative to perform a meta-analysis." @default.
• W3147657356 created "2021-04-13" @default.
• W3147657356 creator A5032700984 @default.
• W3147657356 creator A5058707467 @default.
• W3147657356 creator A5086774720 @default.
• W3147657356 date "2021-03-27" @default.
• W3147657356 modified "2023-10-16" @default.
• W3147657356 title "Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors" @default.
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• W3147657356 doi "https://doi.org/10.3390/ijerph18073492" @default.
• W3147657356 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8036799" @default.
• W3147657356 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33801771" @default.
• W3147657356 hasPublicationYear "2021" @default.
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