Randomization-based inference is a useful alternative to traditional population model-based methods. In trials with missing data, multiple imputation is often used. We describe how to construct a randomization test in clinical trials where multiple imputation is used for handling missing data. We illustrate the proposed methodology using Fisher's combining function applied to individual scores in two post-traumatic stress disorder trials.
Keywords: Analyze as you randomize; Fisher’s combination; minimization; multiple imputation; nonparametric combination of tests; randomization; re-randomization test.