Detecting and adjusting for small-study effects in meta-analysis
Detecting and adjusting for small-study effects in meta-analysis
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DOI：
10.1002/bimj.201000151
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发表时间：
2011-03-01
影响因子：
1.7
通讯作者：
Schwarzer, Guido
中科院分区：
文献类型：
--
作者：
Ruecker, Gerta;Carpenter, James R.;Schwarzer, Guido
发表偏倚和相关类型的小研究效应威胁到系统评价的有效性。实证研究已经证明了小研究效应的存在。小研究效应通过检查漏斗图以图形方式诊断。虽然观察到的漏斗图不对称不能轻易地与一个特定的原因联系起来，但已经提出了基于漏斗图不对称的测试。除了大量的漏斗图检验之外，还有几种方法可以调整这些偏差的治疗效果估计。在本文中，我们将考虑修整和填充方法、Copas选择模型以及最近的基于回归的方法。这些方法使用文献中的荟萃分析来举例说明，并在基于二元响应数据的模拟研究中进行了比较。它们也适用于大量的荟萃分析。讨论了这两种方法之间的一些根本区别。修整填充法和Copas选择模型的一个共同假设是，小研究效应是由选择引起的。修整填充法对应的是由对称假设生成的未知隐式模型，而Copas选择模型是参数统计模型。然而，这需要进行敏感性分析。基于回归的方法更容易实现，而不是基于特定的选择模型。模拟和应用都表明，在存在强选择的情况下，修剪-填充方法和Copas选择模型都可能无法完全消除偏差，而基于回归的方法似乎是一种有希望的替代方法。
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.