GRADE方法中，随机试验起评即为高质量证据，观察性研究起评即为低质量证据；但若证据本身存在高发表偏倚风险，则两者证据质量级别都应降低。即使最佳证据汇总表纳入的各项研究仅有低发表偏倚风险，发表偏倚仍会极大高估效应值。当可得证据来自小样本研究、且多数由厂商资助时，作者应怀疑存在发表偏倚。若干基于检验数据类型的方法可用于评价发表偏倚，其中最常用的为漏斗图，但这些方法都有较大局限。发表偏倚可能较常见，必须特别关注早期结果、对样本量与事件数都很小的早期试验结果尤需小心。
目的 为避免选择和发表偏倚,系统评价者应采用多种查询技术,并尽力获得未发表的研究.本文试图探讨,英特网检索对鉴定未发表和正在进行的临床试验是否有用.研究设计 利用七个Cochrane系统评价的查询策略回顾性地在英特网上检索未纳入的随机对照试验.方法 检索策略 以普通检索式"研究方法学 NEAR干预措施NERA 条件"、用AltaVista在英特网上搜索.测量指标包括搜索时间、英特网搜索已发表研究的回溯率、精确度(已发表和未发表的随机临床试验链接的网页比例)、英特网检索到的未纳入的未发表和正在进行的研究数.结果 用21小时查询了429个网页,找到14个链接到未发表的、正在进行的或最近完成的试验,至少有9个与4篇系统评价相关.英特网检索已发表研究文献的回溯率在0～43.6%,其链接已发表和未发表研究的精确度在0～20.2%.结论 未发表尤其是正在进行的试验的信息可在英特网上找到.潜在的问题是如何评价未经同行评审的电子出版物的质量.急需更强的搜索工具.建议用"Open Trial Initiative"定义英特网发表试验的语法,以加强试验登记的共同操作性.因此,专门的搜索引擎可找到更多有关正在进行和已完成的临床试验信息.
本文介绍证据质量分级的GRADE方法。GRADE将证据质量分为高、中、低和极低四个级别。该分级应用于证据群，而非针对个别研究。系统评价中，质量反映了我们认为效应估计值正确的把握度。对推荐意见而言，质量反映了我们认为效应估计值足以支持某特定推荐的把握度。随机对照试验初定为高质量证据，观察性研究初定为低质量证据。GRADE中所使用的质量一词不只是意味着偏倚风险，还可能受研究结果的不精确性、不一致性和间接性，及发表偏倚的影响。此外，若干因素可增加我们对效应估计值的把握度。GRADE提供了一种系统方法来思考并报告各因素。GRADE将评估证据质量的过程与给出推荐建议的过程分开。推荐强度的判断不仅依赖于证据质量。
The conclusions of meta-analyses are susceptible to various of biases, and publication bias is one of such main bias. Therefore, Checking for evidence of publication bias should be undertaken routinely at the preliminary stage of a meta-analysis. Begg’s test, Egger’s test, and Macaskill’s test are usually used to objectively identify publication bias in meta-analyses. In order to conveniently use these methods, the SAS program of these three tests was designed in this paper. In order test practical data, the fact that the output of this program of SAS software was consisted with the output of STATA software was validated. So, this program is an alternative way to do such hypothesis tests to identify the publication bias in meta-analyses.
This paper introduces the main contents of ROB-ME (Risk Of Bias due to Missing Evidence), including backgrounds, scope of the tool, signal questions and the operation process. The ROB-ME tool has the advantages of clear logic, complete details, simple operation and good applicability. The ROB-ME tool offers considerable advantages for assessing the risk of non-reporting biases and will be useful to researchers, thus being worth popularizing and applying.
Selective non-reporting and publication bias of study results threaten the validity of systematic reviews and meta-analyses, thus affect clinical decision making. There are no rigorous methods to evaluate the risk of bias in network meta-analyses currently. This paper introduces the main contents of ROB-MEN (risk of bias due to missing evidence in network meta-analysis), including tables of the tool, operation process and signal questions. The pairwise comparisons table and the ROB-MEN table are the tool’s core. The ROB-MEN tool can be applied to very large and complex networks including lots of interventions to avoid time-consuming and labor-intensive process, and it has the advantages of clear logic, complete details and good applicability. It is the first tool used to evaluate the risk of bias due to missing evidence in network meta-analysis and is useful to researchers, thus being worth popularizing and applying.