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Modern medical experiments accrue and treat patients--hence obtain treatment response data--throughout a trial. Designs which prospectively plan to modify patient allocation by leveraging accumulating data are response-adaptive randomization (RAR) designs. Many such designs attempt to balance the desire to bias assignment proportions towards a treatment which is performing better against the need to maintain randomization in the face of continued equipoise.
This dissertation consists of simulated investigations into frequentist and ethical properties of an new RAR biased coin design. Chapter 2 proposes a new adaptive design for phase III clinical trials, a modification of the 2001 Bandyopadhyay and Biswas biased coin design. Simulations show how the new design continues to ethically expose patients to the better treatment while simultaneously mitigating power loss inherent in the original design. Chapters 2 and 3 expand the applicability of the new design to scenarios where treatment variances or covariate-treatment impacts are unequal. In Chapter 4, simulations demonstrate that the new response-adaptive biased coin design can be more ethical than equal allocation, even when patient outcomes are not immediately available. Each chapter illustrates the utility and benefits of the new design through a real-world application of an HIV treatment adherence intervention. Asymptotic results are applied to a special case of the BBS design and small sample implications are compared with simulated outcomes in Chapter 5.
Adviser: Kent M. Eskridge