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Inference Using Biased Coin Randomization

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dc.contributor.advisor Rosenberger, William F.
dc.contributor.author Plamadeala, Victoria
dc.creator Plamadeala, Victoria
dc.date 2010-11-30
dc.date.accessioned 2011-05-13T20:10:10Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2011-05-13T20:10:10Z
dc.date.issued 2011-05-13
dc.identifier.uri http://hdl.handle.net/1920/6319
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. en_US
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
thesis.degree.name PhD in Statistical Science en_US
thesis.degree.level Doctoral en
thesis.degree.discipline Statistical Science en
thesis.degree.grantor George Mason University en


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