Maximum Likelihood Estimation and EM Algorithm of Copas Selection Model for Publication Bias Correction

• Activity Number: 70
• Type: Contributed
• Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Jin Piao* and Jing Ning and Yong Chen
• Companies: The University of Texas Health Science Center at Houston and MD Anderson Cancer Center and University of Pennsylvania Perelman School of Medicine
• Keywords: Copas model ; EM algorithm ; Meta analysis ; Publication bias

Abstract

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this paper, we propose a novel expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood.