The quality assessment of non-randomized studies is an important component of a thorough meta-analysis of nonrandomized studies. Low quality studies can lead to a distortion of the summary effect estimate. Recent guidelines for the reporting of meta-analyses of observational studies recommend the assessment of the study quality (MOOSE)[1]. In principal, three categories of quality assessments tools are available: scales, simple checklists, or checklists with a summary judgment (for details see Sanderson et al. 2007 [2]). The results of the quality assessment can be used in several ways such as forming inclusion criteria for the meta-analysis, informing a sensitivity analysis or metaregression, weighting studies, or highlighting areas of methodological quality poorly addressed by the included studies [3]. It has been criticized that the use of summary scores involve inherent weighting of component items including items that may not be related to the validity of the study findings [2].

Sanderson et al.[2] recently identified overall 86 tools for assessing the quality of non-randomized studies. Their review ‘‘highlighted the lack of a single obvious candidate tool for assessing quality of observational epidemiological studies’’[2]. In the field of randomized trials, it has been shown that the choice of quality scale can dramatically influence the interpretation of meta-analyses, and can even reverse conclusions regarding the effectiveness of an intervention [4].