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Meta-analysis combines the results of several scientific studies to obtain a summary quantitative estimation of effect size in order to compare the results of several studies and to identify patterns among them and possible sources of biases. Since the research teams, patients, research protocol, clinical guidelines and time intervals are often different between original scientific studies all these sources of difference can influence the results of each study, causing statistical heterogeneity. Meta-analyses are the highest level of credibility within evidence-based medicine, as they allow us to take into account the influence of many confounding factors and publication biases on the true effect size. Understanding the possible sources of erroneous conclusions in papers will allow researchers to analyze the results of such studies correctly and properly plan their own experiments. In this article the reader will be introduced to methods for identifying and quantifying hidden heterogeneity, such as subgroup analysis and meta-regression. In addition, the reader will learn how to calculate effect size in studies, estimate publication bias mathematically and graphically, and incorporate this estimate into the overall average effect size estimate in meta-analyses.
Tao et al. (Tue,) studied this question.