Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.
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 article, we study a Copas-like selection model and propose an 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.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jing
J
Ning
Department of Biostatistics and Epidemiology, The University of Pennsylvania, Philadelphia, PA 19104, USA.
Yong
Y
Chen
Department of Biostatistics, The University of Texas School of Public Health, Houston, TX 77030, USA.
Jin
J
Piao
D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D017594:Publication Bias
Copas Model; EM algorithm; Meta-analysis; Publication Bias