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Inference Using Biased Coin Randomization

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dc.contributor.advisor Rosenberger, William F. Plamadeala, Victoria
dc.creator Plamadeala, Victoria 2010-11-30 2011-05-13T20:10:10Z NO_RESTRICTION en_US 2011-05-13T20:10:10Z 2011-05-13
dc.description.abstract 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.
dc.language.iso en_US en_US
dc.subject clinical trials en_US
dc.subject randomization tests en_US
dc.subject design-based inference en_US
dc.subject restricted randomization en_US
dc.subject Monte Carlo methods en_US
dc.title Inference Using Biased Coin Randomization en_US
dc.type Dissertation en PhD in Statistical Science en_US Doctoral en Statistical Science en George Mason University en

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