Mean and Mean difference are the two key statistical measures used in the statistical analysis. Both are essential for meta-analysis as well. Mean and Mean difference are used for the interpretation of a large set of values into a single number which explains the heterogeneity and variation among the individual values. However, one of a common challenge in meta-analysis is the unavailability of this data (mean and standard deviation).
Q & A Forum
Meta Analysis
Q: What is the effect size in Meta-Analysis, and which is the good effect size?
Effect Size
The term “effect size” is commonly used in the social sciences, especially in meta-analysis. An effect size is a value that shows the magnitude of the relationship between two groups such as intervention and control. For instance, if a study provides the mean and standard deviation for intervention and control groups, we can calculate the standardized mean difference between two groups. If a study provides the number of events and non-events for two groups, we can calculate an odds ratio. While effect sizes often, but not always, refer to variations of the Standardized Mean Difference (SMD), it is recommended that Cochrane Reviews use the term “SMD” instead of “effect size” to prevent confusion. This distinction helps avoid misinterpretation, as “effect size” is sometimes used in plain language to mean “intervention effect” or “effect estimate.”
It is effect sizes which collectively used to depict the outcome of a study in the meta-analysis.[1]
In the above Meta-analyses Figure, the effect of collagen on skin aging is given by the standard mean difference in a forest plot of meta-analysis. The plot on the right side favours the collagen supplement, while the left side favours the placebo. This review supports the positive effect of collagen on skin aging with a higher standard mean difference which is also referred to as effect size