We examine the use of randomization-based inference for analyzing multiarmed randomized clinical trials, including the application of conditional randomization tests to multiple comparisons. The view is taken that the linkage of the statistical test to the experimental design (randomization procedure) should be recognized. A selected collection of randomization procedures generalized to multiarmed treatment allocation is summarized, and generalizations for two randomization procedures that heretofore were designed for only two treatments are developed. We explain the process of computing the randomization test and conditional randomization test via Monte Carlo simulation, developing an efficient algorithm that makes multiple comparisons possible that would not be possible using a standard algorithm, demonstrate the preservation of type I error rate, and explore the relationship of statistical power to the randomization procedure in the presence of a time trend and outliers. We distinguish between the interpretation of the p-value in the randomization test and in the population test and verify that the randomization test can be approximated by the population test on some occasions. Data from two multiarmed clinical trials from the literature are reanalyzed to illustrate the methodology.
Keywords: Monte Carlo rerandomization test; generalized randomization procedures; multiple treatment comparison; randomization-based inference.
© 2019 John Wiley & Sons, Ltd.