Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Robert A. Perera

Second Advisor

Roy T. Sabo

Third Advisor

Nitai D. Mukhopadhyay

Fourth Advisor

Jessica G. LaRose

Fifth Advisor

Sunny Jung Kim


Response-Adaptive Randomization (RAR) uses outcome data from participants earlier in the trial to skew the allocation of incoming participants towards the better performing treatment arm. However, RAR does not work as intended if the outcomes are distal, as too many (if not all) participants are accrued and randomized before primary outcomes are observed. Thus, we propose different algorithms that use earlier repeated measures of the outcome to predict participants' final repeated measurements (primary outcome) until they’re replaced by the observed primary outcome. These predictions (via predictive modeling) guide RAR by allowing calculation of the target allocation ratio sooner than RAR guided by the primary outcome only. We develop a weighted target allocation ratio that down-weights predicted primary outcomes relative to observed primary outcomes to account for their uncertainty. To evaluate the proposed design, various growth models are compared under a variety of conditions, including different standardized effect sizes, accrual rates, and outcome change patterns. Finally, we also compare our proposed method to a published design in which posterior probabilities from a Bayesian linear mixed model guide adaptive allocation, with a power-loss mitigator embedded (based on Bayesian predictive power) to prevent substantial power loss due to unequal treatment group sizes. Our proposed methods allow the current allocation ratio to more closely reach the optimal target allocation ratio by adaptively allocating sooner than RAR via primary outcome while retaining similar levels of power and Type I error rate and can be suitable alternatives to the already developed Bayesian method.


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Available for download on Monday, August 09, 2027

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