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A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design

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dc.creator Li Yang
dc.date.accessioned 2022-01-25T19:05:53Z
dc.date.available 2022-01-25T19:05:53Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/1920/12306
dc.description.abstract With the rapid development in genomic and genetic research, precision medicine has gained more attention in modern clinical trials. Molecularly targeted therapies are likely to only work with a subgroup of patients. However, the subgroup often will not be identified until after a large scale clinical trial. Clinical trials are often designed under the assumption of no treatment-by-covariate interaction effect and enroll all comers. This makes many patients go through unnecessary treatment and may decrease the efficiency of the trial. In this dissertation, we propose a novel two-stage enrichment design which uses covariate-adjusted response-adaptive (CARA) randomization and a Monte Carlo test to evaluate the interaction effect in the interim analysis for binary and continuous outcomes. A pre-defined alpha level is used as the threshold to decide whether a subgroup will be identified and recruited in the second stage. If a below-threshold interaction effect is found, a regression model will be fitted and the stratum with the largest treatment effect will be chosen as the best stratum. The trial will continue to the second stage with patients from the best stratum only. If the p-value from the interim analysis is above the threshold, the trial continues with all patients. The primary aim is to test the treatment effect between treatment groups. Different CARA procedures are compared in terms of type I error rates, power, and ethical considerations. The CARA procedure that balances better between efficiency and ethics is used in the proposed two-stage enrichment design.
dc.title A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design
thesis.degree.level Ph.D.
thesis.degree.discipline Statistical Science
thesis.degree.grantor George Mason University


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