Random-Effects Meta-Analysis Using Dirichlet Process

• Activity Number: 598
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Saman Muthukumarana*+ and Ram C. Tiwari
• Companies: University of Manitoba and FDA/CDER/OTS/OB
• Keywords: Heterogeneity ; Log pseudo marginal likelihood ; Markov Chain Monte Carlo ; Odds ratio

Abstract

We develop a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process (DP) in meta-analysis is the ability to capture the heterogeneity among studies while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process. We evaluate the performance of the DP approach through simulations and illustrate the proposed method by applying it to three datasets; one large dataset on Program for International Student Assessment (PISA) involving 30 countries, a small dataset from published literature on the treatment of Alzheimers disease and a two-arm clinical trial dataset on preventing mortality after myocardial infarction. Results from the data analyses, simulation studies, and log-pseudo marginal likelihood (LPML) model selection procedure indicate that DP model perform better over conventional alternative methods.