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This is considered to be a strategy to unveil the spread of variance among a population that quite often is treated as one sole group.
The power of the Begg and Mazumdar test is shown in Tables 3 and 4. Best power is achieved for large meta-analyses where the spread of variance is large, the selection strength is strong and the treatment effect is small.
Similar(58)
While we expect these relationships to occur commonly in the data we do not expect all genes to be uniformly up-regulated in one population or down in the other and in the original paper there is a spread of variances to support this view.
For small spread of variances (v=0.5,1.0,2.0), a reasonably correct significance level cannot be guaranteed for k<11.
For a small spread of variances (v=0.5,1.0,2.0), the correct significance level is achieved for k≥18.
Employing a small spread of variances, the significance levels are about 0.09.
When there is a large spread of variances, the simulated distribution of clearly deviates from the theoretical.
When the spread of variances is large (v=0.1,1.0,10.0), a reasonably correct significance level is attained for k=16.
The power is still poor when the spread of variances is small, and the power estimates are below 0.36.
When the spread of variances is large (v=0.1,1.0,10.0), the significance level is roughly 0.02 or lower and deviates considerably from the nominal value of 0.05.
Giving an example, the power equals 0.72 and 0.32 when k=25, a=1.5, δ=0 and the spread of variances is large and small, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com