Types of publication bias and its background are introduced in this paper, and publication bias can be investigated and deal with three methods: funnel plot, trim and filling method, and formula method. Those methods can be used to detect publication bias in conducting systematic reviews.
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.