Kicinski Michal, Springate David A, Kontopantelis Evangelos
Faculty of Science, Hasselt University, Diepenbeek, Belgium.
Centre for Primary Care, National Institute for Health Research School for Primary Care Research, Institute of Population Health, University of Manchester, Manchester, U.K.
Stat Med. 2015 Sep 10;34(20):2781-93. doi: 10.1002/sim.6525. Epub 2015 May 18.
We used a Bayesian hierarchical selection model to study publication bias in 1106 meta-analyses from the Cochrane Database of Systematic Reviews comparing treatment with either placebo or no treatment. For meta-analyses of efficacy, we estimated the ratio of the probability of including statistically significant outcomes favoring treatment to the probability of including other outcomes. For meta-analyses of safety, we estimated the ratio of the probability of including results showing no evidence of adverse effects to the probability of including results demonstrating the presence of adverse effects.
In the meta-analyses of efficacy, outcomes favoring treatment had on average a 27% (95% Credible Interval (CI): 18% to 36%) higher probability to be included than other outcomes. In the meta-analyses of safety, results showing no evidence of adverse effects were on average 78% (95% CI: 51% to 113%) more likely to be included than results demonstrating that adverse effects existed. In general, the amount of over-representation of findings favorable to treatment was larger in meta-analyses including older studies.
In the largest study on publication bias in meta-analyses to date, we found evidence of publication bias in Cochrane systematic reviews. In general, publication bias is smaller in meta-analyses of more recent studies, indicating their better reliability and supporting the effectiveness of the measures used to reduce publication bias in clinical trials. Our results indicate the need to apply currently underutilized meta-analysis tools handling publication bias based on the statistical significance, especially when studies included in a meta-analysis are not recent.
我们使用贝叶斯分层选择模型，对Cochrane系统评价数据库中1106项比较治疗与安慰剂或不治疗的荟萃分析中的发表偏倚进行了研究。对于疗效的荟萃分析，我们估计了纳入支持治疗的具有统计学显著性结果的概率与纳入其他结果的概率之比。对于安全性的荟萃分析，我们估计了纳入显示无不良反应证据的结果的概率与纳入证明存在不良反应的结果的概率之比。
在疗效的荟萃分析中，支持治疗的结果被纳入的概率平均比其他结果高27%（95%可信区间（CI）：18%至36%）。在安全性的荟萃分析中，显示无不良反应证据的结果被纳入的可能性平均比证明存在不良反应的结果高78%（95%CI：51%至113%）。一般来说，在纳入较旧研究的荟萃分析中，有利于治疗的结果的过度呈现量更大。
在迄今为止关于荟萃分析中发表偏倚的最大规模研究中，我们在Cochrane系统评价中发现了发表偏倚的证据。一般来说，在对较新研究的荟萃分析中发表偏倚较小，这表明它们具有更高的可靠性，并支持用于减少临床试验中发表偏倚的措施的有效性。我们的结果表明，需要应用目前未充分利用的基于统计显著性处理发表偏倚的荟萃分析工具，特别是当荟萃分析中纳入的研究不是近期研究时。
