Introduction to Randomization Tests

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Presentation transcript:

Introduction to Randomization Tests 3/12/2009 Copyright © 2009 Dan Nettleton

An Example Suppose I have an instrument that measures the mRNA transcript abundance of a certain gene. I have developed a drug that I suspect will alter the expression of that gene when the drug is injected into a rat. I randomly divide a group of eight rats into two groups of four. Each rat in one group is injected with the drug. Each rat in the other group is injected with a control substance.

Hypothetical Data I use my instrument to measure the expression of the gene in each rat after treatment and obtain the following results: Control Drug Expression 9 12 14 17 18 21 23 26 Average 13 22 The difference in averages is 22-13=9. I wish to claim that this difference was caused by the drug.

Interpretation of the Results Clearly there is some natural variation in expression (not due to treatment) because the expression measures differ among rats within each treatment group. Maybe the observed difference (22-13=9) showed up simply because I happened to choose the rats with larger expression for injection with the drug. What is the chance of seeing such a large difference in treatment means if the drug has no effect?

Random Difference Assignment Control Drug in Averages 1 9 12 14 17 18 21 23 26 9.0 2 9 12 14 18 17 21 23 26 8.5 3 9 12 14 21 17 18 23 26 7.0 4 9 12 14 23 17 18 21 26 6.0 5 9 12 14 26 17 18 21 23 4.5 6 9 12 17 18 14 21 23 26 7.0 7 9 12 17 21 14 18 23 26 5.5 8 9 12 17 23 14 18 21 26 4.5 9 9 12 17 26 14 18 21 23 3.0 10 9 12 18 21 14 17 23 26 5.0 11 9 12 18 23 14 17 21 26 4.0 12 9 12 18 26 14 17 21 23 2.5 13 9 12 21 23 14 17 18 26 2.5 14 9 12 21 26 14 17 18 23 1.0 15 9 12 23 26 14 17 18 21 0.0 69 18 21 23 26 9 12 14 17 -8.5 70 18 21 23 26 9 12 14 17 -9.0 ... ... ... ... ... ... ... ... ... ...

Distribution of Difference between Treatment Means Assuming No Treatment Effect Number of Random Assignments Difference in Treatment Means (Control – Drug)

Conclusions Only 2 of the 70 possible random assignments would have led to a difference between treatment means as large as 9. Thus, under the assumption of no drug effect, the chance of seeing a difference as large as we observed was 2/70 = 0.0286. Because 0.0286 is a small probability, we have reason to attribute the observed difference to the effect of the drug rather than a coincidence due to the way we assigned our experimental units to treatment groups.

Randomization Test This is an example of a randomization test. R.A. Fisher described such tests in the first half of the 20th century. Randomization tests are closely related to permutation tests (almost synonymous) which are popular for assessing statistical significance because they do not rely on specific distributional assumptions.