A Clinical Trial Design With Covariate-Adjusted Response-Adaptive Randomization Using Superiority Confidence of Treatments

Abstract

Adaptive randomization using response outcome or covariate is commonly used in the literature. However, the performance of these designs has not been thoroughly studied, especially when there are various interactions between the covariate and treatment. We have conducted simulations to evaluate the performance of commonly used designs under two-arm and multiple-arm situations. When a predictive factor exists, in the phase II trial conduction using adaptive designs, such as the BATTLE-1, BATTLE-2 trial, and ISPY-2 trials, researchers evaluate the operating characteristics using the traditional power assessment. In this article, new criteria are used in a general modeling frame work to incorporate the complicated interaction. Based on our evaluation, the covariate-adjusted and response-adaptive randomization (Sc-ca) results in a greater total number of responders. Additionally, the design can detect the treatment effect difference in subgroups, and consistently assign patients to the most beneficial treatment according to their covariate profiles. This translates into a higher proportion of individuals receiving optimized treatments compared with other commonly used designs. This adaptive design is a step toward personalized therapy to benefit each patient enrolled in a prospective clinical trial, when there is the strong evidence that predictive factors exist.