Concept
In the original article, k objects (studies) are combined k-1 at a time (jackknife estimation), resulting in k estimates. It is observed that this is a special case of the more general approach of CMA which computes results for k studies taken 1, 2, 3 ... k − 1, k at a time.
Where it is computationally feasible to obtain all possible combinations, the resulting distribution of statistics is termed "exact CMA." Where the number of possible combinations is prohibitively large, it is termed "approximate CMA."
CMA makes it possible to study the relative behaviour of different statistics under combinatorial conditions. This differs from the standard approach in meta-analysis of adopting a single method and computing a single result, and allows significant triangulation to occur, by computing different indices for each combination and examining whether they all tell the same story.
Read more about this topic: Combinatorial Meta-analysis
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