Adaptive urn designs with curtailment for selecting among binary treatments in comparison with a standard

Abstract

Response-adaptive designs in clinical trials use ongoing patient outcome data to adjust the randomization process, aiming to assign more patients to treatments that are showing better performance during the trial. One approach to implement such adaptive randomization is through urn models, which provide a probabilistic framework for updating treatment allocations. In this article, we study the performance of the hybrid selection and testing procedure with curtailment proposed earlier by Buzaianu and Razaila for comparing binomial treatments with a standard, when implemented using the generalized Pólya’s urn design (GPUD) and the cyclic play-the-winner (PWC) design as sampling plans. These adaptive designs meet the same probability requirements in terms of power and size as the curtailment procedure using the vector-at-a-time (VT) sampling studied by Buzaianu and Razaila. Simulations show that integrating curtailment with GPUD and PWC designs significantly lowers the expected sample size compared to the VT design. Moreover, these adaptive designs allocate fewer subjects to inferior treatments relative to both their non-curtailed two-stage counterparts and traditional fixed-sample-size procedures.