Weakly-Informative Group-Specific Prior Distribution for Meta-Analysis

While Bayesian meta-analysis has flourished both in methodological and substantive work, group-specific Bayesian modeling remains scarce. Common practice for choosing prior distributions entails using typical non-informative priors. Currently, there is a push to use more informative prior distributions. In this dissertation I propose a group specific weakly informative prior distribution. The new prior distribution uses a frequentist estimate of between-studies heterogeneity as the noncentrality parameter in a folded noncentral t distribution. This new distribution is then modeled individually for groups based on some categorical factor. An extensive simulation study was performed to assess the performance of the new group-specific prior distribution to several non-informative prior distributions in a variety of meta-analytic scenarios. An application using data from a previously published meta-analysis on dynamic geometry software is also provided.