Explantion of output in comprehensive meta analysis

#EXPLANTION OF OUTPUT IN COMPREHENSIVE META ANALYSIS SOFTWARE#

The first relevant difference concerns the statistical framework used to conduct the analysis. Meta-analyses can widely differ with regard to their methodological and analytical aspects. In this context, this statistical primer is aimed at reviewing standards of methodology and reporting as well as common pitfalls in the interpretation of meta-analyses. In such a context, the advantages of a meta-analysis are intuitive and relevant not only from a clinical but also from a regulatory perspective. Indeed, pooling of the data increases the statistical power to detect differences in low-frequency events. By pooling the results of all available sources, a meta-analysis can address the current gaps in evidence while providing meaningful and directive clinical information. mortality) cannot be drawn from single studies at this moment. Unfortunately, all these trials are relatively modest sized and have been powered for composite clinical end-points so that robust and definitive answers on single hard end-points (i.e. To date, a number of randomized clinical trials have compared the safety and efficacy of surgical aortic valve replacement and transcatheter aortic valve implantation.

As a paradigmatic example, the management algorithm for symptomatic severe aortic stenosis has been recently modified by the introduction of transcatheter aortic valve implantation. In the surgical field, meta-analyses have been instrumental in appraising the clinical value and the comparative efficacy of different treatment strategies. Frequently perceived as the result of mysterious statistical methodologies, de facto, a meta-analysis does not bring more complexity but rather a different way of looking at and analysing data. In modern medicine, the results of a comprehensive and methodologically sound meta-analysis bring the most robust, high-quality information to support evidence-based decision-making. Statistics, Meta-analysis, Clinical evidence INTRODUCTION In this context, this statistical primer aims at providing a conceptual framework, complemented by a practical example, for conducting, interpreting and critically evaluating meta-analyses. random-effect models), no adjustment can be performed in meta-analyses violating the issue of clinical validity and similarity across the included studies. Indeed, even if a reasonable degree of variability across study results (namely, heterogeneity) can be accounted for with proper statistics (i.e. Current recommendations advise a careful selection of the individual studies to be pooled, mainly based on the methodological quality and homogeneity in study designs.

In this regard, guidelines have been introduced to standardize, strengthen and homogenize different aspects of conducting and reporting the results of a meta-analysis.

However, pitfalls are common in this challenging and rapidly evolving field of statistics.

#EXPLANTION OF OUTPUT IN COMPREHENSIVE META ANALYSIS SOFTWARE#

With recent developments in newer meta-analytic approaches, iteration of statistical paradigms and software implementations, network and patient-level meta-analyses have recently gained popularity alongside conventional pairwise study-level meta-analyses.

Explantion of output in comprehensive meta analysis