Webinar on Bayesian outcome-adaptive randomization (OAR) designs
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Bayesian outcome-adaptive randomization (OAR) designs for clinical trials are becoming popular. While traditional designs consider a fixed (e.g., equal) randomization probability during the trial, OAR designs make use of the outcome information obtained for patients already included in the trial to continuously update the probability. As this generally results in more patients being assigned to the ‘more promising’ treatment, based on all current information, the adaptation is suggested to increase patient-specific benefits in clinical trials.
Another extension of the traditional randomized clinical trial designs are ’targeted‘ designs. In these designs, patients are pre-screened by using, e.g., biomarkers, before being randomized to treatments which would be deemed the ‘most promising’ based on patients’ screening results.
Designs combining OAR with the idea of targeted designs have been proposed to avoid the need to pre‑select patients based on their biomarker status. By using OAR it is possible to assign patients within a particular biomarker stratum to the ‘most promising’ treatment arms during the course of the trial. Additionally, it is also possible to stop accrual to ‘non-promising’ treatments during the trial.
Implementation of the Bayesian (biomarker-driven) OAR designs is not trivial. Elements such as the selection of the prior distributions, early-stopping criteria, or biomarker-assay accuracy strongly influence operational characteristics of the designs. Also, while the designs may offer advantages (in terms of, for instance, a reduced total target sample size or a decrease in the variation of the accrued sample size), several issues have been identified, including, among others, statistical inefficiency due to imbalance in the number of patients assigned to different treatment arms and a non-trivial probability of ending up with a substantially larger number of patients assigned to the less-efficient treatment arm.