# Multiple testing correction of already corrected values

I have a list of say 50,000 e-values (multiple testing corrected P values) for a statistical test I am doing that essentially matches patterns together. Essentially the e-value is the expectation that these two patterns would match purely by chance.

Most people who do this will do another round of multiple testing correction on their list of e-values which are already corrected for the number of tests that generate that e-value. For example each E-value may be the result of 100,000 tests matching different patterns together. The theory is when you have 50,000 of these e-values you will still have a good number of type I error's associated with your list.

Can someone give me a some theory on why it's a good idea to apply multiple testing correction to values that were already corrected for multiple testing?

Is there a good paper I can read that will help me understand? Conceptually I can understand why you want to do this, because that list of 50,000 e-vales will have some false positives, but if there is a paper someone can point me to that would be great.

$p_{s}=\underset{k}{min}\: mp_{(k)} /k$
where $m$ is the number of tests performed (50000 in your example) and $k$ is the number of times the test was applied (100000 in your case if I am not mistaken?). Keep in mind that there might be p-value dependence issues but Simes argues that from numerical evidence this correction still performs well. Have a look also at https://www.researchgate.net/publication/2255729_Combining_Statistical_Tests_By_Multiplying_p-values. This the source of many parts of the anwser and I have applied the Simes solution myself regarding the application of different statistical tests on the same data.