Ning Jing, Chen Yong, Piao Jin
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Biostatistics and Epidemiology, The University of Pennsylvania, Philadelphia, PA 19104, USA.
Biostatistics. 2017 Jul 1;18(3):495-504. doi: 10.1093/biostatistics/kxx004.
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.
发表偏倚是指已发表的研究结果系统地不能代表所开展研究的总体情况，这对有意义的荟萃分析构成潜在威胁。Copas选择模型为校正估计提供了一个灵活的框架，并对发表偏倚提供了相当多的见解。然而，在Copas选择模型下最大化观察到的似然性具有挑战性，因为观察到的数据包含关于潜在变量的信息非常少。在本文中，我们研究了一个类似Copas的选择模型，并基于完全似然性提出了一种期望最大化（EM）算法进行估计。实证模拟研究表明，EM算法及其相关的推断程序表现良好，并且在最大化观察到的似然性时避免了不收敛问题。
