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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-bycovariate 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, anddoi:10.1007/s12561-015-9125-9 pmid:27606036 pmcid:PMC5010868 fatcat:f4uxl7mpqbbnbm5bzclqfb74ki