Using Meta-Analysis Results in Bayesian Updating: The Empty-Cell Problem

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Abstract
Bayesian estimation incorporating prior information has been a popular approach to gaining estimation efficiency. Although prior information can take a variety of forms, generalizations derived from meta-analyses have been suggested as being useful. This article shows that these priors can be problematic in light of the many empty cells observed in meta-analysis designs. Design reduction, which gives rise to an unbiased prior, is found to be the preferred solution.