Robust Shrinkage Estimation of Effect Sizes via Bayesian Random-Effects Models for Meta-Analysis

• In Event: Hierarchical Linear Models

Abstract

In the case of meta-analyses with outlying study results, Empirical Bayes estimates of outliers based on conventional random-effects models are often shrunk to an average effect size by an excessive amount. This can be problematic especially when attempting to answer substantive questions concerning how large the largest effect size in a given sample of studies might be. In order to address this issue, we employ a fully Bayesian approach specifying t-distributional assumptions for random effects. The empirical data-analysis and simulation study results show that the Bayes-t models with heavy tails provide robust shrinkage estimates of outliers, thus protecting against over-shrinkage that can arise under maximum likelihood estimation, or when Bayesian models assuming normally-distributed random effects are used.

Authors

• Junok Kim, University of California - Los Angeles
  Presenting Author

• Michael H. Seltzer, University of California - Los Angeles
  Non-Presenting Author