# An introduction to randomization for clinical trials 2

Published on September 29, 2016   20 min
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#### Other Talks in the Series: The Risk of Bias in Randomized Clinical Trials

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My name is William F. Rosenberger. I'm an University Professor and Chairman of the Department of Statistics at George Mason University. I also have written two books on the subject of randomization. This is part two of "An introduction to randomization for clinical trials" recorded for Henry Stewart Talks. I recommend that you watch the first part of "An introduction to randomization for clinical trials" before proceeding to part two.
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Now, I want to discuss another criterion which is randomization as a basis for inference.
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So the third great property of randomization is that it provides a basis for inference, that's assumption-free and relies only on the way in which the subjects were randomized. The early clinical trialists were aware of the importance of randomization-based inference, but as I mentioned earlier, they had limited computer resources to implement it. Nowadays, we can run a randomization test, or as the FDA calls it, a "re-randomization" test in seconds, just by modifying the program that we used to generate the initial sequence. Unfortunately, many students are not taught randomization tests anymore, or even told that the usual population model does not apply in clinical trials. Randomization tests are particularly useful for small clinical trials, where standard large sample theory tests may not apply.
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So if we think about the usual population model, based on random sampling from a population, we can think of two populations, populations A and B. And then we do random sampling. And then presumably, the sample that we get will be an "i.i.d" sample with the same population model as what we sampled from and parameters based on those populations. We call it θA and θB. Clinical trials do not follow this model, so often what's done is we invoke a population or a random sampling model to describe a clinical trial in order to conduct inference. In this case, we have basically an unspecified patient population because they really aren't populations of patients taking on experimental therapy or even taking a placebo. Then there's some undefined sampling procedure from this population that produces "n" patients and then randomization is conducted. And we get "nA" patients on treatment A, "nB" patients on treatment B. And we assume this "i.i.d" model with parameters θA and θB.
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