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: Why is standard error preferred over total sample size on the vertical axis?

Standard error is preferred because it accounts for additional factors influencing statistical power, such as:

Number of events in dichotomous outcomes.
Standard deviation in continuous outcomes.
Effect size variation across studies.

A large study with few events may have less power than a smaller study with many events, making standard error a more appropriate measure.

Refernce:

Cochrane Handbook for Systematic Reviews of Interventions. (2011). 10.4.1 Funnel plots. The Cochrane Collaboration. Retrieved from

https://handbook-5-1.cochrane.org/chapter_10/10_4_1_funnel_plots.htm

Q: Why is standard error preferred over total sample size on the vertical axis?

Refernce:

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