Carpenter James R, Schwarzer Guido, Rücker Gerta, Künstler Rita
Institute of Medical Biometry and Medical Informatics, University Medical Center, Freiburg, Germany.
J Clin Epidemiol. 2009 Jun;62(6):624-631.e4. doi: 10.1016/j.jclinepi.2008.12.002. Epub 2009 Mar 12.
Although using meta-analysis to combine evidence from a number of studies should reduce both bias and uncertainty, it is sometimes not the case, because published studies represent a biased selection of the evidence. Copas proposed a selection model to assess the sensitivity of meta-analysis conclusions to possible selection bias. However, this relatively complex model awaits both reliable software and an empirical evaluation. This article reports work addressing both these issues.
We took 157 meta-analyses with binary outcomes, analyzed each one using the Copas selection model, and evaluated each analysis using a prespecified protocol. The evaluation aimed to assess the usefulness of the Copas selection model to a typical Cochrane reviewer.
In approximately 80% of meta-analyses, the overall interpretation of the Copas selection model was clear, with better results among the 22 with evidence of selection bias. However, as with the "Trim and Fill" method, allowing for selection bias can result in smaller standard errors for the treatment estimate.
When a reliable test for selection bias is significant, we recommend systematic reviewers to try the Copas selection model, although the results should be interpreted cautiously.
尽管使用荟萃分析来整合多项研究的证据应能减少偏差和不确定性，但有时情况并非如此，因为已发表的研究是对证据的有偏选择。科帕斯提出了一种选择模型，以评估荟萃分析结论对可能的选择偏差的敏感性。然而，这个相对复杂的模型既需要可靠的软件，也需要实证评估。本文报告了解决这两个问题的工作。
我们选取了157项具有二元结局的荟萃分析，使用科帕斯选择模型对每一项进行分析，并使用预先指定的方案对每一项分析进行评估。该评估旨在评估科帕斯选择模型对典型的考克兰综述作者的有用性。
在大约80%的荟萃分析中，科帕斯选择模型的总体解释是清晰的，在有选择偏差证据的22项分析中结果更好。然而，与“修剪与填充”方法一样，考虑选择偏差可能会导致治疗估计的标准误差更小。
当对选择偏差的可靠检验具有显著性时，我们建议系统综述作者尝试使用科帕斯选择模型，尽管对结果的解释应谨慎。
