Randomization-based analysis of covariance for inference in the sequential parallel comparison design

Randomization tests in clinical trials with multiple imputation for handling missing data

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.