Exact Inference for Random Effect Model in Meta-Analysis

• Activity Number: 515
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Lu Tian*+ and Lee-Jen Wei
• Companies: Stanford University and Harvard University
• Keywords: meta analysis ; random effect model ; robust inference ; exact confidence interval

Abstract

The performance of the standard parametric random effects modeling inference in meta analysis is sensitive to the parametric assumption as well as the number of studies. When the number of studies involved is not big, the widely used DerSimonian-Laird interval for the average treatment effect may not be able to cover the true parameter at the desired nominal level. In this talk, we will present a new class of confidence intervals based on robust test statistics. This new meta-analysis technique can be easily implemented with study-level summary statistics as the standard method. However, in contrast to existing methods for parametric random effects models, the validity of our proposal does not require the number of studies involved to be large. The proposed procedure has robust empirical performance based on our extensive numerical study. We will then use the procedure to analyze some real data examples.