Stats & Methodology

What's the Multiple Comparison Problem? | Statistics

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What’s the Multiple Comparison Problem? | Statistics

The multiple comparison problem is the issue that arises when multiple tests on the same sample are performed. An example will illustrate this.

Eg.

Let’s say that a study looks at prospective risk factors for running injuries in 5000 novice runners. Different variables are tested, since we do not yet know which ones will increase the risk. Examples are: running volume, navicular drop, q-angle, quad and glute strength, heel vs forefoot strike pattern, minimalist vs maximalist shoe, and ankle dorsiflexion ROM.

Most researchers will accept a 5% false positive rate, the alpha or significance level. This is for a given variable like quad strength. It means that if this study is conducted one hundred times, about 5 studies will show a false positive result, when in fact, there is none.

However, the researchers are looking at ten variables, not just quad strength; this within the same sample. This poses a problem.

The researchers, unbeknownst to this problem, conduct the trial. Two years later the data comes in, showing a heel strike pattern and glute strength to be a risk factors for a running injury. Great! That’s the conclusion and the paper gets published.

As noted before, the significance level at 5% does not mean there is a 5% false positive rate at this point due to the plethora of different variables that are being researched. So the researchers implicitly accepted a much greater risk for false positive results by conducting the trial, looking at ten variables.

The family wise error rate demonstrates this. With a quite simple calculation we can check the false positive rate, it is 40%! The formula is shown below.

I think we can agree that this forms a problem. So what are we going to do about it? There is a solution. Researchers can make corrections to counteract this alpha-inflation by doing a Bonferroni or Holm correction. This is discussed in “Type 1 error rate control”.

Family-wise error rate formula:

1 – (1 – ɑ)x

ɑ: alpha or significance level in decimals

x: number of tests

References

Wason, J. M. S., & Robertson, D. S. (2021). Controlling type I error rates in multi-arm clinical trials: A case for the false discovery rate. Pharmaceutical statistics, 20(1), 109–116.

Dudoit, S., van der Laan, M. J., & Pollard, K. S. (2004). Multiple testing. Part I. Single-step procedures for control of general type I error rates. Statistical applications in genetics and molecular biology, 3, Article13.

Lakens, D., Type 1 error control by Daniel Lakens, youtube

John Ludbrook (1998). MULTIPLE COMPARISON PROCEDURES UPDATED. , 25(12), 1032–1037. doi:10.1111/j.1440-1681.1998.tb02179.x

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