Importance of Meta Analysis

In the field of statistics, the term ‘meta analysis’ refers to the different methods that contrasts and combines the outcome from various studies in order to identify certain patterns in those results, the reasons for disagreement among those, or any kind of relationships. In fact, meta analysis is carried out by identifying a common measure belonging to an effect size, of which the final output is the weighted average of the same. Here, the weighting corresponds to the sample sizes included in individual studies.

Generally, meta analysis aims to take an estimate of the actual effect size against a less accurate one which has been derived from a particular study conducted under a given set of conditions and assumptions. Therefore, a meta analysis gives the summary of different studies conducted on the same subject, thus giving the reader a wide knowledge regarding the existence and size of the effect.

In some cases, meta analysis is used for systematic review. For example, meta analysis is conducted on various clinical trials of a particular medical treatment so as to gain better knowledge about the effectiveness of the same. Meta analysis forms a part of the network namely estimation statistics which mainly relies upon confidence intervals, effect sizes, precision planning for data analysis, etc. Further, meta analysis can be used instead of null hypothesis significance testing.

Moreover, meta analysis is widely used in studies related to cost-effectiveness where the outcome from several studies are combined to determine the most effective of therapies, the estimation of the amount of effectiveness when it comes to different choices, and also the estimation of cost-related outcomes in expensive therapies.

When a meta analysis is conducted, it is necessary to include a main research question in the research protocol, a summary of the intended studies, inclusion or exclusion parameters for selecting a particular trial, and methods used for trial selection.

To summaries, meta analysis is extensively used in different fields of study.

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