Randomization and Permutation Tests

Vance W. Berger (), Patrick Onghena () and J. Rosser Matthews ()
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Vance W. Berger: National Cancer Institute, Biometry Research Group
Patrick Onghena: KU Leuven, Faculty of Psychology and Educational Sciences
J. Rosser Matthews: Defense and Veterans Brain Injury Center, General Dynamics Health Solutions

Chapter 94 in Principles and Practice of Clinical Trials, 2022, pp 1851-1867 from Springer

Abstract: Abstract This chapter will address the decision to use permutation tests as opposed to parametric analyses in the context of between-group analysis in randomized clinical trials designed to evaluate a medical intervention. It is important to understand at the outset that permutation tests represent a means to an end, rather than an end unto themselves. It is not so much that one seeks to use permutation tests just for the sake of doing so but, rather, that one recognizes the severe deficiencies of parametric analyses and wishes to use some other type of analysis that does not similarly suffer from these drawbacks. When viewed in this context, properly conducted permutation tests are the solution to the problem of how to compare treatments without having to rely on assumptions that cannot possibly be true. We argue that the default position would clearly be the use of exact analyses and that the burden of proof would fall to those who would argue that the approximate analyses are just as good or, as is sometimes argued, even better.

Keywords: Approximations; Normality; Parametric analyses; Precautionary principle (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-52636-2_129

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DOI: 10.1007/978-3-319-52636-2_129

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