Oklahoma State University Center for Health Sciences, Tulsa, OK, USA.
Ann Oncol. 2017 May 1;28(5):931-937. doi: 10.1093/annonc/mdw691.
Publication bias is an over-representation of statistically significant results in the published literature and may exaggerate summary effect estimates in oncology systematic reviews. Omitting non-significant results in systematic reviews may therefore affect clinical decision-making. We investigate ways that systematic reviewers attempted to limit publication bias during the search process as well as the statistical methods used to evaluate it. For a subset of reviews not reporting publication bias evaluations, we carried out our own assessments for publication bias to determine its likelihood among these reviews.
We examined systematic reviews from the top five highest impact factor oncology journals published between 2007 and 2015. Systematic reviews were screened for eligibility and qualifying reviews (n = 182) were coded for relevant publication bias study characteristics by two authors. A re-analysis of reviews not initially evaluating for publication bias was carried out using Egger's regression, trim-and-fill, and selection models.
Of the 182 systematic reviews, roughly half carried out a hand search to locate additional studies. Conference abstracts were the most commonly reported form of gray literature, followed by clinical trials registries. Fifty-one reviews reported publication bias evaluations. The most common method was the funnel plot (80%, 41/51) followed by Egger's regression (59%, 30/51) and Begg's test (43%, 22/51). Our publication bias evaluations on non-reporting reviews suggest that the degree of publication bias depends on the method employed.
Our study shows publication bias assessments are not frequently used in oncology systematic reviews. Furthermore, evidence of publication bias was found in a subset of non-reporting reviews. Systematic reviewers in oncology are encouraged to conduct such analyses when appropriate and to employ more robust methods for both mitigating and evaluating publication bias.
发表偏倚是指在已发表的文献中过度呈现具有统计学意义的结果，这可能会夸大肿瘤系统评价中的汇总效应估计。因此，系统评价中省略无统计学意义的结果可能会影响临床决策。我们研究了系统评价者在搜索过程中试图限制发表偏倚的方法，以及用于评估发表偏倚的统计方法。对于未报告发表偏倚评估的综述的一个子集，我们自己进行了发表偏倚评估，以确定这些综述中发表偏倚的可能性。
我们检查了 2007 年至 2015 年间发表在影响因子最高的五个肿瘤学期刊上的系统评价。对系统评价进行了筛选，以确定其是否符合纳入标准，符合纳入标准的合格评价（n=182）由两位作者对相关发表偏倚研究特征进行了编码。对最初未评估发表偏倚的综述进行了重新分析，使用 Egger 回归、trim-and-fill 和选择模型。
在 182 篇系统评价中，大约有一半进行了手工搜索以查找其他研究。会议摘要报告为最常见的灰色文献形式，其次是临床试验注册处。51 篇综述报告了发表偏倚评估。最常用的方法是漏斗图（80%，41/51），其次是 Egger 回归（59%，30/51）和 Begg 检验（43%，22/51）。我们对未报告综述的发表偏倚评估表明，发表偏倚的程度取决于所采用的方法。
我们的研究表明，发表偏倚评估在肿瘤学系统评价中并不常用。此外，在非报告综述中发现了发表偏倚的证据。鼓励肿瘤学系统评价者在适当的时候进行此类分析，并采用更稳健的方法来减轻和评估发表偏倚。
