Rücker Gerta, Carpenter James R, Schwarzer Guido
Institute of Medical Biometry and Medical Informatics, University Medical Center, Stefan-Meier-Strasse 26, D-79104 Freiburg, Germany.
Biom J. 2011 Mar;53(2):351-68. doi: 10.1002/bimj.201000151. Epub 2011 Jan 14.
Publication bias and related types of small-study effects threaten the validity of systematic reviews. The existence of small-study effects has been demonstrated in empirical studies. Small-study effects are graphically diagnosed by inspection of the funnel plot. Though observed funnel plot asymmetry cannot be easily linked to a specific reason, tests based on funnel plot asymmetry have been proposed. Beyond a vast range of funnel plot tests, there exist several methods for adjusting treatment effect estimates for these biases. In this article, we consider the trim-and-fill method, the Copas selection model, and more recent regression-based approaches. The methods are exemplified using a meta-analysis from the literature and compared in a simulation study, based on binary response data. They are also applied to a large set of meta-analyses. Some fundamental differences between the approaches are discussed. An assumption common to the trim-and-fill method and the Copas selection model is that the small-study effect is caused by selection. The trim-and-fill method corresponds to an unknown implicit model generated by the symmetry assumption, whereas the Copas selection model is a parametric statistical model. However, it requires a sensitivity analysis. Regression-based approaches are easier to implement and not based on a specific selection model. Both simulations and applications suggest that in the presence of strong selection both the trim-and-fill method and the Copas selection model may not fully eliminate bias, while regression-based approaches seem to be a promising alternative.
发表偏倚及相关类型的小研究效应威胁着系统评价的有效性。小研究效应的存在已在实证研究中得到证实。通过检查漏斗图可直观诊断小研究效应。尽管观察到的漏斗图不对称性难以轻易与特定原因联系起来，但基于漏斗图不对称性的检验方法已被提出。除了众多的漏斗图检验方法外，还有几种针对这些偏倚调整治疗效应估计值的方法。在本文中，我们考虑了修剪填充法、科帕斯选择模型以及最近基于回归的方法。这些方法通过文献中的一项荟萃分析进行举例说明，并基于二元反应数据在模拟研究中进行比较。它们还被应用于大量的荟萃分析。文中讨论了这些方法之间的一些根本差异。修剪填充法和科帕斯选择模型共有的一个假设是小研究效应是由选择导致的。修剪填充法对应于由对称性假设生成的未知隐式模型，而科帕斯选择模型是一个参数统计模型。然而，它需要进行敏感性分析。基于回归的方法更易于实施，且不基于特定的选择模型。模拟和应用均表明，在存在强烈选择的情况下，修剪填充法和科帕斯选择模型可能都无法完全消除偏倚，而基于回归的方法似乎是一个有前景的替代方法。
