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Stat Med. 2017 Aug 15;36(18):2831-2843. doi: 10.1002/sim.7329. Epub 2017 May 2.

The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials.

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Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, 02215, MA, U.S.A.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, 02115, MA, U.S.A.


We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters.


cluster-randomized trials; pair-matched design; permutation test; stepped-wedge design; time-to-event endpoints

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