Inference Using Biased Coin Randomization

We 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.

Author

• Plamadeala, Victoria

• Clinical trials
• Randomization tests
• Design-based inference
• Restricted randomization
• Monte Carlo methods

Identifier

hdl:1920/6319

OAI identifier oai:identifier

oai:MARS:1920/6319