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Statistics in Biosciences

, Volume 8, Issue 1, pp 77–98 | Cite as

Permutation Testing for Treatment–Covariate Interactions and Subgroup Identification

  • Jared C. Foster
  • Bin Nan
  • Lei Shen
  • Niko Kaciroti
  • Jeremy M. G. Taylor
Article

Abstract

We consider the problem of using permutation-based methods to test for treatment–covariate interactions from randomized clinical trial data. Testing for interactions is common in the field of personalized medicine, as subgroups with enhanced treatment effects arise when treatment-by-covariate interactions exist. Asymptotic tests can often be performed for simple models, but in many cases, more complex methods are used to identify subgroups, and non-standard test statistics proposed, and asymptotic results may be difficult to obtain. In such cases, it is natural to consider permutation-based tests, which shuffle selected parts of the data in order to remove one or more associations of interest; however, in the case of interactions, it is generally not possible to remove only the associations of interest by simple permutations of the data. We propose a number of alternative permutation-based methods, designed to remove only the associations of interest, but preserving other associations. These methods estimate the interaction term in a model, then create data that “looks like” the original data except that the interaction term has been permuted. The proposed methods are shown to outperform traditional permutation methods in a simulation study. In addition, the proposed methods are illustrated using data from a randomized clinical trial of patients with hypertension.

Keywords

Permutation tests Treatment–covariate interactions Subgroup analysis Personalized medicine 

Notes

Acknowledgments

This research was partially supported by a Grant from Eli Lilly, Grant DMS-1007590 from the National Science Foundation, Grants CA083654 and AG036802 from the National Institutes of Health (NIH), and the Intramural Research Program of the NIH, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). We also utilized the high-performance computational capabilities of the Biowulf Linux cluster at NIH, Bethesda, MD. (http://biowulf.nih.gov).

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Copyright information

© International Chinese Statistical Association (outside the US) 2015

Authors and Affiliations

  • Jared C. Foster
    • 1
  • Bin Nan
    • 2
  • Lei Shen
    • 3
  • Niko Kaciroti
    • 2
  • Jeremy M. G. Taylor
    • 2
  1. 1.Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaUSA
  2. 2.Department of BiostatisticsUniversity of MichiganAnn ArborUSA
  3. 3.Global Statistical Sciences, Advanced AnalyticsEli LillyIndianapolisUSA

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