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Randomization-based inference and the choice of randomization procedures

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A Publisher Correction to this article was published on 12 September 2019

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Abstract

In testing the significance of treatment effects in randomized clinical trials (RCTs), randomization-based inference is distinguished from population-based parametric and nonparametric inference, such as the t-test or permutation tests, taking into account three properties: preservation of type I error rate, relation of power to the randomization procedure, and flexibility in choosing the test statistic. In this paper, we revisit rationale of the properties and provide justification through simulations. We propose that the choice of randomization procedures and the analysis of RCTs can be facilitated by the application of randomization-based inference.

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Change history

  • 12 September 2019

    Unfortunately, due to a technical error, the articles published in issues 60:2 and 60:3 received incorrect pagination. Please find here the corrected Tables of Contents. We apologize to the authors of the articles and the readers.

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Authors and Affiliations

  1. Department of Statistics, George Mason University, 4400 University Drive MS4A7, Fairfax, VA, 22030, USA

    Yanying Wang & William F. Rosenberger

  2. The George Washington University Biostatistics Center, 6110 Executive Boulevard Suite 750, Rockville, MD, 20852, USA

    Diane Uschner

Authors
  1. Yanying Wang
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  2. William F. Rosenberger
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  3. Diane Uschner
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Corresponding author

Correspondence to William F. Rosenberger.

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Wang, Y., Rosenberger, W.F. & Uschner, D. Randomization-based inference and the choice of randomization procedures. Stat Papers 60, 395–404 (2019). https://doi.org/10.1007/s00362-018-01070-y

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  • DOI: https://doi.org/10.1007/s00362-018-01070-y

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