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 might generic effect size interpretations be problematic?
The real-world importance of an effect size is highly context-dependent, meaning that a small effect in one setting may still be clinically significant, while a large effect in another may not be meaningful. Therefore, effect sizes should always be considered within the context of the specific research question and patient population rather than relying solely on general rules of thumb.
References:
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Altman, D. G. (1996). Bland Altman analysis for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307–310.
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