{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/6319"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/6319","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Inference Using Biased Coin Randomization","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Plamadeala, Victoria"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-13","date_published":"2011-05-13","updated_at":"2026-07-27T19:52:04Z","subjects":["Clinical trials","Randomization tests","Design-based inference","Restricted randomization","Monte Carlo methods"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/6319"],"render_values":[{"text":"hdl:1920/6319","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2011-05-13"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Clinical trials","Randomization tests","Design-based inference","Restricted randomization","Monte Carlo methods"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/6319"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["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."]},{"key":"dc:title","label":"Title","values":["Inference Using Biased Coin Randomization"]}]}],"canonical_facts":{"dc:date.issued":["2011-05-13"],"dc:description.other":["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":["hdl:1920/6319"],"dc:subject":["Clinical trials","Randomization tests","Design-based inference","Restricted randomization","Monte Carlo methods"],"dc:title":["Inference Using Biased Coin Randomization"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:52:04Z"}