Rosenberger, William F.Plamadeala, Victoria2011-05-13NO_RESTRIC2011-05-132011-05-13https://hdl.handle.net/1920/6319We provide a novel approach to approximate conditional randomization tests fol- lowing Efron's randomization procedure by sampling from the conditional reference set. We use combinatorial algebra to derive the conditional distribution of the num- ber of subjects randomized to a treatment. The result is a simple and e±cient Monte Carlo technique that is invariant to the total sample size, the degree of imbalance between treatments, the choice of test statistic, or the biased coin parameter. More- over, it provides an unbiased and strongly consistent estimator for the conditional randomization test p{value. Additionally, the technique is easily extended to the approximation of conditional strati¯ed randomization tests. Finally, sampling from the conditional reference set enables the approximation of conditional randomization tests when sequential monitoring is performed in the course of the experiment.en-USClinical trialsRandomization testsDesign-based inferenceRestricted randomizationMonte Carlo methodsDissertation