Rejoinder to "quantifying publication bias in meta-analysis".
Rejoinder to "quantifying publication bias in meta-analysis".
复制标题
反驳“量化荟萃分析中的发表偏差”。
DOI：
10.1111/biom.12815
复制
发表时间：
2018
期刊：
影响因子：
1.9
通讯作者：
Chu,Haitao
中科院分区：
文献类型：
--
作者：
Lin,Lifeng;Chu,Haitao
我们感谢共同编辑组织了这次讨论，也感谢丹·杰克逊博士、克里斯托弗·施密德博士和南希·盖勒博士（以下分别称为DJ、CS和NG）对我们工作的出色讨论。所有三位讨论者都指出了在荟萃分析中评估发表偏倚的重要性以及这样做的困难。事实上，发表偏倚充满了不确定性：发表标准如何导致偏倚在不同情况下有很大差异。研究可能被禁止发表，因为它们的p值在统计学上不显著（Copas等人，二〇一三年; Citkowicz和Vevea，2017年），或其效应量太负（Duval和Tweedie，2000年a），或其样本量太小（Tang和Liu，2000年），如NG和CS所讨论的。由于这种差异，没有一种方法可以在所有情况下都表现良好。同时，这些困难保证了未来的研究，探索所提出的方法的性能，使用更多的模拟和案例研究，所有三个讨论者的建议。正如CS所讨论的，发表偏倚的缺失数据机制可能不是随机缺失的，因此在各种设置下进行广泛的模拟需要大量的工作，超出了本反驳的范围，但我们相信这些工作可以为文献做出很好的贡献，类似于Duval和Tweedie（2000 b），Macaskill等人的模拟。（2001），Peters et al.（2006），以及Bürkner和Doebler（2014）。除了这些不确定性，甚至“出版偏见”这个名字也有点争议。与选择模型相比，基于漏斗图的方法往往更受青睐，可能是因为检查漏斗图的不对称性是直观的。然而，这种不对称可能由发表偏倚以外的原因引起（例如，研究质量差），并且经常与其他偏倚来源混淆，例如CS提到的报告偏倚（Schmid，2017）。因此，一些研究人员更喜欢将这个问题描述为“小研究效应”（e。例如，在一个实施例中，Harbord等人，2006），而不是出版偏见。然而，小型研究的影响可能只反映了漏斗图不对称性的一个方面。与其他基于漏斗图不对称性的发表偏倚检测方法一样，我们的方法无法区分漏斗图的不对称性是由于发表偏倚还是其他偏倚来源。研究人员需要使用其他证据，并仔细检查漏斗图的不对称性是否真的是由于出版偏见。此外，与诊断或校正分析相比，更需要防止发表偏倚（Lau等人，2006年）。正如CS所建议的，通过广泛搜索广泛的资源可以减少发表偏倚;试验的前瞻性注册也可以帮助防止发表偏倚（罗斯坦等人，正如DJ所指出的，我们打算强调测量出版物偏倚，而不仅仅是对其进行测试，因为前者很少被考虑。然而，我们并不想阻止研究人员进行发表偏倚测试。“测量”和“测试”是不同但密切相关的概念，两者都可以提供有关发表偏倚的有价值的信息。测量必须具有某些特征;例如，它们应该对治疗效果的规模和荟萃分析中的研究数量保持不变。到目前为止，大多数关于发表偏倚的研究都集中在假设检验上。然而，这对研究人员来说是不够的，他们可能想量化发表偏倚的严重程度。在我们的文章中考虑的发表偏倚的措施，回归截距TI和偏度TS，可以帮助我们评估发表偏倚的严重程度在荟萃分析。目前，回归…
We thank the co-editor for organizing the discussion and Drs. Dan Jackson, Christopher Schmid, and Nancy Geller (henceforth, DJ, CS, and NG, respectively), for their outstanding discussion of our work. All three discussants pointed out the importance of assessing publication bias in metaanalysis as well as the difficulties of doing so. Indeed, publication bias is full of uncertainties: how publication criteria lead to bias varies greatly case by case. Studies may be suppressed from publication because their p-values are not statistically significant (Copas et al., 2013; Citkowicz and Vevea, 2017), or their effect sizes are too negative (Duval and Tweedie, 2000a), or their sample sizes are too small (Tang and Liu, 2000), as discussed by NG and CS. Because of this difference, no method can perform well in all cases. Meanwhile, these difficulties warrant future research on exploring the proposed methods’ performance using more simulations and case studies, as suggested by all three discussants. As discussed by CS, the missing data mechanism for publication bias is probably missing not at random, so conducting extensive simulations under various settings requires a considerable amount of work and is beyond the scope of this rejoinder, yet, we believe such work could make a good contribution to the literature, similar to the simulations by Duval and Tweedie (2000b), Macaskill et al.(2001), Peters et al.(2006), and Bürkner and Doebler (2014). In addition to these uncertainties, even the name “publication bias” is a bit controversial. Compared with selection models, funnel-plot-based methods tend to be favored possibly because checking a funnel plot’s asymmetry is intuitive. However, such asymmetry may arise from causes other than publication bias (eg, poor study quality) and it is often confused with other sources of bias, such as reporting bias mentioned by CS (Schmid, 2017). Hence, some researchers prefer to describe the problem as “small-study effects”(e. g., Harbord et al., 2006), instead of publication bias. However, small-study effects may only reflect one aspect of a funnel plot’s asymmetry. Like other publication bias detection methods based on asymmetry in funnel plots, our methods cannot distinguish whether a funnel plot’s asymmetry is due to publication bias or other sources of bias. Researchers need to employ other evidence and carefully examine whether a funnel plot’s asymmetry is truly due to publication bias. Moreover, compared with diagnostic or corrective analysis, prevention of publication bias is more desirable (Lau et al., 2006). As suggested by CS, publication bias may be diminished by extensively searching for a wide range of resources; prospective registration of trials may also help prevent publication bias (Rothstein et al., 2005).As DJ noted, we intended to emphasize measuring publication bias instead of merely testing for it, because the former has seldom been considered. However, we did not mean to discourage researchers from testing for publication bias.“Measuring” and “testing” are different but strongly related concepts, and both can provide valuable information about publication bias. Measures must possess certain features; for example, they should be invariant to the scale of treatment effects and the number of studies in a meta-analysis. So far, most works on publication bias have focused on hypothesis testing. However, this is insufficient for researchers, who may want to quantify how serious publication bias is. The measures of publication bias considered in our article, the regression intercept TI and the skewness TS, can help us evaluate the severity of publication bias in a meta-analysis. Currently, the regression …