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: How can confidence intervals help assess clinical significance?
Confidence intervals can be used to determine the significance of an intervention for the intended effect.
For example, if an drug intervention reduces risk from 35% to 30%, it may be recognized clinically useful only if the risk reduction is at least 5 percentage points.
- Shorter 95% CI (e.g., 8% to 12%) → Confirms intervention is beneficial.
- Broader 95% CI (e.g., 3% to 19%) → indicates some benefit, but also includes very less effects.
- Very broader CI including 0% (e.g., -3% to 11%) → Cannot exclude no effect at all, requiring attention.
Thus, confidence intervals are essential for establishing whether an effect is both statistically and clinically beneficial.
Refernce:
Cochrane Handbook for Systematic Reviews of Interventions. (2011). 12.4.1 Confidence intervals. The Cochrane Collaboration. Retrieved from
https://handbook-5-1.cochrane.org/chapter_12/12_4_1_confidence_intervals.htm