Inference Using Biased Coin Randomization
| 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 | |
| dc.date.available | 2011-05-13T20:10:10Z | |
| dc.date.issued | 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.identifier.uri | https://hdl.handle.net/1920/6319 | |
| dc.language.iso | en_US | |
| dc.subject | Clinical trials | |
| dc.subject | Randomization tests | |
| dc.subject | Design-based inference | |
| dc.subject | Restricted randomization | |
| dc.subject | Monte Carlo methods | |
| dc.title | Inference Using Biased Coin Randomization | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Statistical Science | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | PhD in Statistical Science |