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Activity Number: 262
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319942
Title: Relative Performance of Heterogeneity Variance Estimators in Research Synthesis and Meta-Analysis
Author(s): Kepher H. Makambi and Hanfei H. Xu*
Companies: Georgetown University and Georgetown University
Keywords: Random effects model ; DerSimonian-Laird estimator ; normal and binary response ; bias ; mean squared error

In random effects meta-analysis, estimation of the heterogeneity variance followed by inference on the overall treatment effect parameter is an outstanding problem that has been addressed over the years without consensus. This study focuses on methods for estimating the heterogeneity variance in the random effects model. A detailed simulation study is performed to compare eighteen heterogeneity variance estimators with respect to bias and mean square error for normal and binary responses. Measures of effect size considered include mean differences, probability differences, and log odds ratios. AN example from the extant literature is used to illustrate the application of the procedures.

Authors who are presenting talks have a * after their name.

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