Meta Analysis is the procedure of applying statistical principles to various conceptually similar scientific studies to estimate the common truth behind all individual studies. All individual studies that are conceptually similar arrive at this common truth with a certain degree of error. Therefore, the statistical integration of the results will provide an estimate closest to the common truth.
It can be said that Meta Analysis is the analysis of analyses.
By comparing results of different studies, Meta Analysis also helps in comparing and contrasting the results of individual studies, identifying patterns among the studies and their effects, gauging the sources of disagreement among the studies, and other exciting relationships.
In performing a Meta Analysis, each study is allocated different weightage according to certain aspects associated with the study (for example, comparing a study that had 100 participants with a study of 10 participants needs due considerations) and the weighted average is calculated accordingly.
Initially and still predominantly used for clinical trials, the procedure improves accuracy to the point estimate of the closest truth as more scientifical studies are taken into consideration. The analysis helps in quantifying and analysing inconsistencies among individual studies. The different process involved within meta-analysis helps various purposes like spotting and investigating publication bias present in different studies, explaining variation between results due to factors like moderator inclusion,
As a general principle, generating, summarizing, and understanding the best available evidence are essential for establishing the benefits and safety of interventions. A well designed and implemented Meta Analysis study can be a great benefit to society. While Meta Analysis has the potential to be a powerful tool in evaluating health care treatments and interventions, there are many potential pitfalls and problems that are yet to be resolved.
A poorly performed Meta Analysis can perpetuate biases from ill-conceived studies or lead to false conclusions. This, in turn, can cause consumers and caregivers, who frequently access results of Meta Analysis through websites and popular press, to form incorrect conclusions and can result in inappropriate medical decisions. While the idea behind combining studies to improve precision and power is straightforward, the actual implementation of the process is difficult. Those who act or react based on meta-analysis should understand the various biases that could be incorporated into a review.
Examples of some potential pitfalls in meta-analysis include publication bias, pipeline bias, and English language bias. In addition, combining studies that are not similar in study design, population, methods of analysis or outcome definitions can lead to biases as well, which may result in spurious conclusions being drawn.
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