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Meta-analysis is a statistical analysis that incorporates several results of scientific research. Meta-analysis can be accomplished when several scientific studies address the same issue, with each individual study record measurements assuming some degree of error.
The goal is then to use statistical methods to extract a pooled approximation nearest to the unk-nown common truth based on the interpretation of this error.
Network meta-analysis compares multiple approaches simultaneously by evaluating experiments that make different correlations in the same analysis. It is a difficult task to decide which one to choose when numerous treatment options are available.
The importance of network meta analysis lies in taking decisions explicitly instead of taking implicitly.
Direct treatment comparison refers to the traditional meta-analysis which is performed using research groups that explicitly (head-to-head) evaluate the same two treatments.
It is the comparison of different approaches in health care using data from separate research. Indirect comparison is often used due to the dearth of evidence from head-to-head comparative studies or insufficient evidence.
For example, using indirect treatment comparison; the advantage of A over B is obtained by means of comparing the trails of A vs C to that B vs C, even though this treatment fairly results in inaccurate estimates. Characterization of three or more comparison treatments as a structure of multiple comparison evidence are performed depending on the comparative studies conducted pair-wise.
Over time, available therapies are constantly increasing for many medical indications. For example, for the treatment of depression and other mental disorders, a wide range of different–old and new–medications are available. Physicians, clinicians and health policymakers often have to find out the most cost-effective treatment. Well performed randomized controlled trials provide the most accurate assessments of the comparative efficacy of competing for healthcare approaches. Nevertheless, in randomized controlled trials, most methods were not measured directly. If there is zero or inadequate data from direct comparison trials, the outcomes of different studies may be used to assess the effects of different treatments. Compared to direct comparison within the study, indirect comparison implies a comparison of different procedures between the studies.
Standard meta-analysis acts as an effective tool for evidence base medicine in spite of its drawback of comparing only two alternative treatments at a time. However, if there are no studies that compare two strategies explicitly, it is impossible to measure their comparative effectiveness.
Multiple treatment meta-analysis therapies integrates both direct and indirect comparison data from a network of trials using multiple procedures to evaluate as widely and precisely as possible the findings of summary treatment.
Mixed/Multiple treatment comparison (MTC) is nothing but the generalized form of generic pair meta- analysis for A Vs B to that of data systems. For Example: consider the study on A Vs B, B Vs C and A Vs C. MTC performs two roles where it strengthens the relative effectiveness of two therapies using both direct and indirect comparisons and other one is to select the best treatment by simultaneously allowing all treatments interferences.