Pros and cons of permutation tests in clinical trials

Stat Med. 2000 May 30;19(10):1319-28. doi: 10.1002/(sici)1097-0258(20000530)19:10<1319::aid-sim490>;2-0.


Hypothesis testing, in which the null hypothesis specifies no difference between treatment groups, is an important tool in the assessment of new medical interventions. For randomized clinical trials, permutation tests that reflect the actual randomization are design-based analyses for such hypotheses. This means that only such design-based permutation tests can ensure internal validity, without which external validity is irrelevant. However, because of the conservatism of permutation tests, the virtues of permutation tests continue to be debated in the literature, and conclusions are generally of the type that permutation tests should always be used or permutation tests should never be used. A better conclusion might be that there are situations in which permutation tests should be used, and other situations in which permutation tests should not be used. This approach opens the door to broader agreement, but begs the obvious question of when to use permutation tests. We consider this issue from a variety of perspectives, and conclude that permutation tests are ideal to study efficacy in a randomized clinical trial which compares, in a heterogeneous patient population, two or more treatments, each of which may be most effective in some patients, when the primary analysis does not adjust for covariates. We propose the p-value interval as a novel measure of the conservatism of a permutation test that can be defined independently of the significance level. This p-value interval can be used to ensure that the permutation test have both good global power and an acceptable degree of conservatism.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Humans
  • Models, Statistical*
  • Randomized Controlled Trials as Topic / standards
  • Randomized Controlled Trials as Topic / statistics & numerical data*